Adaptive interface for continuous monitoring devices

ABSTRACT

Systems and methods that continuously adapt aspects of a continuous monitoring device based on collected information to provide an individually tailored configuration are described. The adaptations may include adapting the user interface, the alerting, the motivational messages, the training, and the like. Such adaptation can allow a patient to more readily identify and understand the information provided by/via the device.

INCORPORATION BY REFERENCE TO RELATED APPLICATION

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 14/525,622, filed Oct. 28, 2014, which claims the benefit of U.S.Provisional Application No. 61/898,300 filed Oct. 31, 2013. Theaforementioned application is incorporated by reference herein in itsentirety, and is hereby expressly made a part of this specification.

FIELD OF THE INVENTION

The present development relates generally to medical devices such as acontinuous glucose sensor, including systems and methods for adaptiveinterface processing of sensor data.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high glucose, which may cause an array ofphysiological derangements (for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye) associated with the deteriorationof small blood vessels. A hypoglycemic reaction (low glucose) may beinduced by an inadvertent overdose of insulin, or after a normal dose ofinsulin or glucose-lowering agent accompanied by extraordinary exerciseor insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricks to obtain blood samples for measurement. Due to the lack ofcomfort and convenience associated with finger pricks, a person withdiabetes normally only measures his or her glucose levels two to fourtimes per day. Unfortunately, time intervals between measurements can bespread far enough apart that the person with diabetes finds out too lateof a hyperglycemic or hypoglycemic condition, sometimes incurringdangerous side effects. It is not only unlikely that a person withdiabetes will take a timely SMBG value, it is also likely that he or shewill not know if his or her blood glucose value is going up (higher) ordown (lower) based on conventional methods. Diabetics thus may beinhibited from making educated insulin therapy decisions.

Another device that some diabetics use to monitor their blood glucose isa continuous analyte sensor. A continuous analyte sensor typicallyincludes a sensor that is placed subcutaneously, transdermally (e.g.,transcutaneously), or intravascularly. The sensor measures theconcentration of a given analyte within the body, and generates a rawsignal that is transmitted to electronics associated with the sensor.The raw signal is converted into an output value that is displayed on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful information, such as blood glucose expressed in mg/dL.Unfortunately, wide spread adoption of continuous analyte sensors hasbeen hindered because of the “one size fits all” approach to systemdesigns thus far.

SUMMARY

A system that continuously adapts based on collected information toprovide an individually tailored configuration is believed to improvewidespread adoption by adaptively and interactively improving healthcareassistance for chronic management of disease in an ambulatory setting.The adaptations may include adapting the user interface, the alerting,the motivational messages, the training, and the like. Such adaptationcan allow a patient to more readily identify and understand theinformation provided by/via the device.

In a first innovative aspect, a method for adaptive configuration of ananalyte monitoring device is provided. The method includes transmittinga first report of physiological information of a subject using a firstreporting format, wherein the first reporting format comprises a firstreporting format characteristic. The method further includes determiningat least one of behavioral or contextual information comprising at leastone behavioral or contextual characteristic for the subject. The methodalso includes comparing the at least one behavioral and/or contextualcharacteristic with one or more behavioral or contextual criteria. Themethod additionally includes adjusting the reporting format based atleast in part on the comparing, wherein the reporting format comprises asecond reporting format characteristic that is different from the firstreporting format characteristic. The method further includestransmitting a second report of physiological information using thesecond reporting format.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, the first report comprises a trend graph of the physiologicalinformation over a period of time.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, determining the behavioral or contextual information includesreceiving a message from a sensor including data associated with apatient, identifying a characteristic extractor based on the message andthe sensor, generating, via the identified characteristic extractor, theat least one behavioral or contextual characteristic based on thereceived message, and associating the generated characteristic with thebehavioral or contextual information.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, comparing the characteristic with one or more behavioral orcontextual criteria includes comparing the characteristic with abehavioral or contextual criteria associated with a goal.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, comparing the characteristic with one or more behavioral orcontextual criteria comprises comparing the characteristic with abehavioral or contextual criteria associated with an interfaceadaptation. In some implementations, the interface adaptation comprisesat least one of an alert frequency, an alert volume, an alert tone, adisplay font, a display font size, a display font color, a messagedelivery address, a message delivery telephone number, a listing of menuitems, or an operational setting for the analyte monitoring device.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, the method further includes transmitting a message identifyingthe adjustment, and upon receipt of a confirmation of the adjustment,activating the second reporting format for subsequent reporting, as wellas, upon receipt of a denial of the adjustment or no response to themessage, activating the first reporting format for subsequent reporting.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, the first report is transmitted to a common destination as thesecond report.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the firstaspect, the first report is transmitted to a first destination and thesecond report is transmitted to a second destination.

In a second innovative aspect, a method of identifying adaptationinformation for an individual is provided. The method includes capturingvalues from pre-identified inputs, the values indicating a behavior orcontext associated with a physiological condition for the individual.The method further includes periodically storing additional valuesreceived from the pre-identified inputs, wherein a record ofuser-specific pre-identified input values is created. The method furtherincludes periodically determining behavioral or contextual informationabout the individual based on the record of user-specific pre-identifiedinput values.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, the method further includes transmitting the determinedbehavioral or contextual information about the patient.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, transmitting includes transmitting to a continuous monitoringdevice, a patient record system, a smartphone, or a social mediainternet site.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, the pre-identified inputs include at least one of a glucometer,a thermometer, an accelerometer, a camera, a microphone, a queryprocessing engine, an electronic device configured formachine-to-machine communication, or an electronic patient record.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, periodically storing additional values comprises storing atimestamp indicating when a specific additional value was stored.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, the physiological condition comprises one or more of diabetes,obesity, malnutrition, hyperactivity, depression, or fertility.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, determining behavioral or contextual information about theindividual includes selecting one of a plurality of pre-identified inputvalues included in the record, and identifying one or more behavior orcontext based on a comparison of the selected input value and the inputproviding the selected value with an identification value associatedwith a plurality of behaviors or contexts.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, determining behavioral or contextual information about theindividual comprises processing the pre-defined input values included inthe record.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the secondaspect, processing the values comprises identifying a trend for thevalues.

In a third innovative aspect, a method of adaptive goal setting for anindividual is provided. The method includes obtaining first behavioralor contextual information associated with the individual. The methodfurther includes obtaining second behavioral or contextual informationassociated with a plurality of individuals having a commoncharacteristic with the individual. The method includes generating oneor more behavioral or contextual criteria for the goal based on theobtained first information and the obtained second information. Themethod also includes generating the goal based on the generatedcriteria.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the thirdaspect, the method also includes providing a predetermined goalincluding at least one behavioral or contextual criteria, and whereingenerating the goal comprises modifying the at least one behavioral orcontextual criteria of the predetermined goal based on the generated oneor more behavioral or contextual criteria.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the thirdaspect, the method also includes providing the generated goal forpresentation via a human detectable interface and receiving a messageactivating the goal.

In a fourth innovative aspect, a method of adaptive guidance isprovided. The method includes identifying a need related to trainingneed or requested guidance based on behavioral/contextual informationfor the user. The method also includes providing training or guidance inresponse to the identified need, wherein the training or guidance isbased on physiological information in conjunction with thebehavioral/contextual information for the user.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the fourthaspect, the behavioral or contextual information includes a structuredquery or a natural language query.

In a generally applicable embodiment (i.e., independently combinablewith any of the aspects or embodiments identified herein) of the fourthaspect, the method also includes receiving feedback associated with thetraining or guidance and re-processing the training or guidance infurther consideration of the received feedback.

In a fifth innovative aspect, an integrated system for monitoring aglucose concentration in a host and for delivering insulin to the hostis provided. The system includes a continuous glucose sensor, whereinthe continuous glucose sensor is configured to substantiallycontinuously measure a glucose concentration in the host, and to providecontinuous sensor data associated with the glucose concentration in thehost. The system further includes an insulin delivery device configuredto deliver insulin to the host, wherein the insulin delivery device isoperably connected to the continuous glucose sensor. The system alsoincludes a processor module configured to perform, in whole or in part,any one of the four innovative methods described above.

In a sixth innovative aspect, an electronic device for monitoring aglucose concentration in a host is provided. The device includes acontinuous glucose sensor, wherein the continuous glucose sensor isconfigured to substantially continuously measure a glucose concentrationin the host, and to provide continuous sensor data associated with theglucose concentration in the host. The device further includes aprocessor module configured to perform, in whole or in part, any one ofthe four innovative methods described above.

In a seventh innovative aspect, an electronic device for deliveringinsulin to a host is provided. The device includes an insulin deliverydevice configured to deliver insulin to the host, wherein the insulindelivery device is operably connected to the continuous glucose sensor.The device also includes a processor module configured to perform, inwhole or in part, any one of the four innovative methods describedabove.

In an eighth innovative aspect, a system for adaptive configuration ofan analyte monitoring device is provided. The system includes an inputreceiver configured to receive at least one of context information,behavior information, or physiological information for a user over aperiod of time. The system includes an input processor configured toidentify a context or behavior based at least in part on the informationreceived over time. The system further includes an adaptation engineconfigured to determine an adaptation for the analyte monitoring devicebased on the identified context or behavior.

Any of the features of an embodiment of the first, second, third,fourth, fifth, sixth, seventh or eighth aspects is applicable to allaspects and embodiments identified herein. Moreover, any of the featuresof an embodiment of the first, second, third, fourth, sixth, seventh oreighth aspects is independently combinable, partly or wholly with otherembodiments described herein in any way, e.g., one, two, or three ormore embodiments may be combinable in whole or in part. Further, any ofthe features of an embodiment of the first, second, third, fourth, orfifth, sixth, seventh or eighth aspects may be made optional to otheraspects or embodiments. Any aspect or embodiment of a method can beperformed by a system or apparatus of another aspect or embodiment, andany aspect or embodiment of a system can be configured to perform amethod of another aspect or embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an integrated system of the preferredembodiments, including a continuous glucose sensor and a medicamentdelivery device.

FIG. 2 is a process flow diagram of a method of adjusting an interfaceformat/style of physiological information based on behavioral and/orcontextual information.

FIG. 3 shows a plot of the histogram (or distribution) of carbs in ameal for an individual over a period of 30 days.

FIG. 4 is a process flow diagram of a method of determining behavioraland/or contextual information for a patient.

FIG. 5 is a process flow diagram of a method of determining goals orcriteria for use in one or more aspects described.

FIG. 6 is a process flow diagram of a method of providing patienttraining, improvement in diabetes management, and/or short termrecommendation.

FIG. 7 is a functional block diagram for a continuous monitoring deviceincluding an adaptive interface.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Consider the specific example of continuous glucose monitoring. Fordiabetics, the glucose monitor can literally be the difference betweenlife and death. Characteristics of the users of glucose monitors vary onmany dimensions. Each has their own medical needs. Each user has anindividual level of technical sophistication. Each user has their uniqueeducational background, language, and cultural references. Each userparticipates in their own set of activities at varying degrees ofintensity. Furthermore, characteristics are not static, that is, theymay change over time. These are but a few factors which may influencehow, when, and why a patient uses (or chooses to ignore) their glucosemonitor.

One non-limiting advantage of the described features is to provide aninterface which is adapted to the patient. The adaptation considersactive and/or passive data associated with the patient to generate aninterface suited to the patient's unique characteristics and behavior.Rather than adjusting the interface statically via preferences, dynamictailoring of the interface can help improve the patient's experiencewith the monitoring device and ultimately achieve more consistent andaccurate usage of the device in compliance with a prescribed treatmentplan. Furthermore, the system may be configured to identify adaptationsin real-time such that the system continually assesses ways in which itand/or the user may adjust to maximize the desirable outcome(s) as thedata becomes available.

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

To ease the understanding of the described features, continuous glucosemonitoring is used as part of the explanations that follow. It will beappreciated that the adaptive systems and methods described areapplicable to other continuous monitoring systems. For example, thefeatures discussed may be used for continuous monitoring of lactate,free fatty acids, heart rate during exercise, IgG-anti gliadin, insulin,glucagon, movement tracking, fertility, caloric intake, hydration,salinity, sweat/perspiration (stress), ketones, adipanectin, troponin,perspiration, and/or body temperature. Where glucose monitoring is usedas an example, one or more of these alternate examples of monitoringconditions may be substituted.

The term “continuous glucose sensor,” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and generally refers to a device that continuouslyor continually measures the glucose concentration of a bodily fluid(e.g., blood, plasma, interstitial fluid and the like), for example, attime intervals ranging from fractions of a second up to, for example, 1,2, or 5 minutes, or longer. It should be understood that continual orcontinuous glucose sensors can continually measure glucose concentrationwithout requiring user initiation and/or interaction for eachmeasurement, such as described with reference to U.S. Pat. No.6,001,067, for example.

The phrase “continuous glucose sensing” or “continuous glucosemonitoring” as used herein are a broad terms, and to be given theirordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andgenerally refers to the period in which monitoring of the glucoseconcentration of a host's bodily fluid (e.g., blood, serum, plasma,extracellular fluid, etc.) is continuously or continually performed, forexample, at time intervals ranging from fractions of a second up to, forexample, 1, 2, or 5 minutes, or longer. In one exemplary embodiment, theglucose concentration of a host's extracellular fluid is measured every1, 2, 5, 10, 20, 30, 40, 50 or 60-seconds.

The term “substantially” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and generally refers to being largely but not necessarilywholly that which is specified, which may include an amount greater than50 percent, an amount greater than 60 percent, an amount greater than 70percent, an amount greater than 80 percent, an amount greater than 90percent or more.

The terms “processor” and “processor module,” as used herein are a broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and generally refers to a computersystem, state machine, processor, or the like designed to performarithmetic or logic operations using logic circuitry that responds toand processes the basic instructions that drive a computer. In someembodiments, the terms can include ROM and/or RAM associated therewith.

As used herein, the term “host” generally refers to animals (e.g.,mammals such as humans) and plants. The terms “subject” and “individual”can be used interchangeably with the term “host,” in certainembodiments. As used herein, the term “user” generally refers to one whoutilizes information obtained from a sensor, inputs information into asensor, or otherwise interacts with a sensor, directly or via aninterface. In certain embodiments a user may be a host, e.g., anindividual who utilizes data from a continuous glucose sensor forself-monitoring of glucose levels. In certain embodiments the user andthe host are different, e.g., a parent who utilizes data from acontinuous glucose sensor for monitoring a child's glucose levels or ahealthcare worker who utilizes data from a continuous glucose sensor forselecting an insulin treatment protocol for a patient with diabetes.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of the analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, and/orintravascular (e.g., intravenous) device. In some embodiments, thedevice can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose-measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor, the systems and methods of embodiments can be applied to anymeasurable analyte. In some embodiments, the analyte sensor is a glucosesensor capable of measuring the concentration of glucose in a host. Someexemplary embodiments described below utilize an implantable glucosesensor. However, it should be understood that the devices and methodsdescribed herein can be applied to any device capable of detecting aconcentration of analyte and providing an output signal that representsthe concentration of the analyte.

In some embodiments, the analyte sensor is an implantable glucosesensor, such as described with reference to U.S. Pat. No. 6,001,067 andU.S. Patent Publication No. US-2011-0027127-A1. In some embodiments, theanalyte sensor is a transcutaneous glucose sensor, such as describedwith reference to U.S. Patent Publication No. US-2006-0020187-A1. In yetother embodiments, the analyte sensor is a dual electrode analytesensor, such as described with reference to U.S. Patent Publication No.US-2009-0137887-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1. The patentsand publications are incorporated herein by reference in their entirety.

FIG. 1 is a block diagram of an integrated system of the preferredembodiments, including a continuous glucose sensor and a medicamentdelivery device. FIG. 1 shows an exemplary environment in which someembodiments described herein may be implemented. Here, an analytemonitoring system 100 includes a continuous analyte sensor system 8.Continuous analyte sensor system 8 includes a sensor electronics module12 and a continuous analyte sensor 10. The system 100 can also includeother devices and/or sensors, such as a medicament delivery pump 2 and areference analyte meter 4, as illustrated in FIG. 1. The continuousanalyte sensor 10 may be physically connected to sensor electronicsmodule 12 and may be integral with (e.g., non-releasably attached to) orreleasably attachable to the continuous analyte sensor 10.Alternatively, the continuous analyte sensor 10 may be physicallyseparate to sensor electronics module 12, but electronically coupled viainductive coupling or the like. Further, the sensor electronics module12, medicament delivery pump 2, and/or analyte reference meter 4 maycommunicate with one or more additional devices, such as any or all ofdisplay devices 14, 16, 18 and 20.

The system 100 of FIG. 1 also includes a cloud-based processor 22configured to analyze analyte data, medicament delivery data and/orother patient related data provided over network 24 directly orindirectly from one or more of sensor system 8, medicament delivery pump2, reference analyte meter 4, and display devices 14, 16, 18, 20. Basedon the received data, the processor 22 can further process the data,generate reports providing statistic based on the processed data,trigger notifications to electronic devices associated with the host orcaretaker of the host, or provide processed information to any of theother devices of FIG. 1. In some exemplary implementations, thecloud-based processor 22 comprises one or more servers. If thecloud-based processor 22 comprises multiple servers, the servers can beeither geographically local or separate from one another. The network 24can include any wired and wireless communication medium to transmitdata, including WiFi networks, cellular networks, the Internet and anycombinations thereof.

It should be understood that although the example implementationdescribed with respect to FIG. 1 refers to analyte data being receivedby processor 22, other types of data processed and raw data may bereceived as is described in further detail herein.

In some exemplary implementations, the sensor electronics module 12 mayinclude electronic circuitry associated with measuring and processingdata generated by the continuous analyte sensor 10. This generatedcontinuous analyte sensor data may also include algorithms, which can beused to process and calibrate the continuous analyte sensor data,although these algorithms may be provided in other ways as well. Thesensor electronics module 12 may include hardware, firmware, software,or a combination thereof to provide measurement of levels of the analytevia a continuous analyte sensor, such as a continuous glucose sensor.

The sensor electronics module 12 may, as noted, couple (e.g., wirelesslyand the like) with one or more devices, such as any or all of displaydevices 14, 16, 18, and 20. The display devices 14, 16, 18, and/or 20may be configured for processing and presenting information, such sensorinformation transmitted by the sensor electronics module 12 for displayat the display device. The display devices 14, 16, 18, and 20 can alsotrigger alarms based on the analyte sensor data.

In FIG. 1, display device 14 is a key fob-like display device, displaydevice 16 is a hand-held application-specific computing device 16 (e.g.,the DexCom G4® Platinum receiver commercially available from DexCom,Inc.), display device 18 is a general purpose smart phone or tabletcomputing device 20 (e.g., an Apple® iPhone®, iPad®, or iPod touch®commercially available from Apple, Inc.), and display device 20 is acomputer workstation 20. In some exemplary implementations, therelatively small, key fob-like display device 14 may be a computingdevice embodied in a wrist watch, a belt, a necklace, a pendent, a pieceof jewelry, an adhesive patch, a pager, a key fob, a plastic card (e.g.,credit card), an identification (ID) card, and/or the like. This smalldisplay device 14 may include a relatively small display (e.g., smallerthan the display device 18) and may be configured to display a limitedset of displayable sensor information, such as a numerical value 26 andan arrow 28. Some systems may also include a wearable device 21, such asdescribed in U.S. Patent Application No. 61/896,597 filed Oct. 28, 2103,and entitled “Devices Used in Connection with Continuous AnalyteMonitoring that Provide the User with One or More Notifications, andRelated Methods,” the entire disclosure of which is hereby expresslyincorporated by reference. The wearable device 21 may include anydevice(s) that is/are worn on, or integrated into, a user's vision,clothes, and/or bodies. Example devices include wearable devices,anklets, glasses, rings, necklaces, arm bands, pendants, belt clips,hair clips/ties, pins, cufflinks, tattoos, stickers, socks, sleeves,gloves, garments (e.g. shirts, pants, underwear, bra, etc.), “clothingjewelry” such as zipper pulls, buttons, watches, shoes, contact lenses,subcutaneous implants, cochlear implants, shoe inserts, braces (mouth),braces (body), medical wrappings, sports bands (wrist band, headband),hats, bandages, hair weaves, nail polish, artificial joints/body parts,orthopedic pins/devices, implantable cardiac or neurological devices,etc. The small display device 14 and/or the wearable device 21 mayinclude a relatively small display (e.g., smaller than the displaydevice 18) and may be configured to display graphical and/or numericalrepresentations of sensor information, such as a numerical value 26and/or an arrow 28. In contrast, display devices 16, 18 and 20 can belarger display devices that can be capable of displaying a larger set ofdisplayable information, such as a trend graph 30 depicted on thehand-held receiver 16 in addition to other information such as anumerical value and arrow.

It is understood that any other user equipment (e.g., computing devices)configured to at least present information (e.g., a medicament deliveryinformation, discrete self-monitoring analyte readings, heart ratemonitor, caloric intake monitor, and the like) can be used in additionor instead of those discussed with reference to FIG. 1.

In some exemplary implementations of FIG. 1, the continuous analytesensor 10 comprises a sensor for detecting and/or measuring analytes,and the continuous analyte sensor 10 may be configured to continuouslydetect and/or measure analytes as a non-invasive device, a subcutaneousdevice, a transdermal device, and/or an intravascular device. In someexemplary implementations, the continuous analyte sensor 10 may analyzea plurality of intermittent blood samples, although other analytes maybe used as well.

In some exemplary implementations of FIG. 1, the continuous analytesensor 10 may comprise a glucose sensor configured to measure glucose inthe blood using one or more measurement techniques, such as enzymatic,chemical, physical, electrochemical, fluorescent, spectrophotometric,polarimetric, calorimetric, iontophoretic, radiometric, immunochemical,and the like. In implementations in which the continuous analyte sensor10 includes a glucose sensor, the glucose sensor may be comprise anydevice capable of measuring the concentration of glucose and may use avariety of techniques to measure glucose including invasive, minimallyinvasive, and non-invasive sensing techniques (e.g., fluorescentmonitoring), to provide a data, such as a data stream, indicative of theconcentration of glucose in a host. The data stream may be raw datasignal, which is converted into a calibrated and/or filtered data streamused to provide a value of glucose to a host, such as a user, a patient,or a caretaker (e.g., a parent, a relative, a guardian, a teacher, adoctor, a nurse, or any other individual that has an interest in thewellbeing of the host). Moreover, the continuous analyte sensor 10 maybe implanted as at least one of the following types of sensors: animplantable glucose sensor, a transcutaneous glucose sensor, implantedin a host vessel or extracorporeally, a subcutaneous sensor, arefillable subcutaneous sensor, an intravascular sensor.

In some implementations of FIG. 1, the continuous analyte sensor system8 includes a DexCom G4® Platinum glucose sensor and transmittercommercially available from DexCom, Inc., for continuously monitoring ahost's glucose levels.

FIG. 2 is a process flow diagram of a method of adjusting an interfaceformat/style (e.g., graph, display of sensor data, buttons, alarms,default screens, and preferences for interaction) of physiologicalinformation based on behavioral and/or contextual information. Theadaptive reporting process 200 shown in FIG. 2 recognizes a patternbased on one or more inputs that can adaptively trigger support/partnermode. Recognizing the context of the patient, the adaptive reportingprocess 200 may cause the device to switch into a context appropriatemode of operation. The context appropriate mode may be adapted todetermine how long, how much content (e.g., alert, alarm, screen,information, etc.) to present via an output of the device, how muchcontent was ignored, daily reset, thereby subtly taking over withouteffort from the user. In some implementations, the adaptation may bebased on a pre-set goal such as a target glucose reading.

The process of providing automatic adaptation of a user interfacefeature (e.g., settings or functions) based on behavioral and/orcontextual information solves a long felt need of personalizing generalpurpose devices for healthcare use, or health care devices intended foruse with a wide variety of user preferences, in a simple and intuitivemanner. That is, not all users have the same preferences when it comesto personalization of interface format/style of physiologicalinformation of a medical device (e.g., user interface settings orfunctions for health care data), especially a consumer-driven medicaldevice. Some users may be tech savvy and enjoy reviewing large amountsof their health data; other users may prefer a simpler interaction. Manyusers fall in between and their preferences may be influenced by thecontext surrounding their interaction. Unfortunately, creating devicesthat are highly customizable also tend to be highly complex, and viceversa, therefore do not address the full spectrum of users. Thereremains a need for automatically and adaptively understanding thebehavior and context of the user of a consumer-driven medical devicethat is highly intelligent and allows for adaptation of the userinterface. In one implementation, these needs are met by providing areport in a first matter/style (202); determining behavior or contextualinformation (204); comparing the behavior or contextual information witha goal or criteria (206); adjusting the reporting format based on thecomparison (208) and providing a report in the adjusted format (210), adevice may be efficiently, intuitively and intelligently personalizedfor optimizing health care management and use, without requiring acomplex or comprehensive understanding of technology, human behavior (orcontext) and health data that would otherwise extremely difficult,inefficient and likely impossible for a human to perform as is madepossible by the systems and methods described herein.

“Adaptive reporting” generally refers to the process, quality, or act ofupdating or changing the reporting style or format (e.g., of a humandetectable interface associated with the medical device or its data)based on received information such that a functional feature isadjusted. In some implementations, the adaptation is based on predictiveinferences drawn from the information collected for the associated user.An adaptive reporting system or method may be contrasted with a reactive(or dynamic) reporting system or method. Whereas a reactive or dynamicreporting system or method may provide a single reactive adjustment inreal time based on a single event or selection (e.g., in reaction to astimulus), an adaptive reporting system or method anticipates the eventbased on the previous behavioral or contextual patterns identified forthe user over time and makes an ongoing adjustment to a setting orfunctionality based thereon. One non-limiting benefit of adaptivereporting systems and methods in accordance with some implementations isto avoid the occurrence of an undesirable event altogether by adjustingthe system in anticipation of the negative event.

The adaptive reporting process 200 shown in FIG. 2 may be implemented inwhole or in part using a continuous monitoring system such as thedevices shown and described in FIG. 1. The adaptive reporting process200 may be implemented in hardware such as via a field programmable gatearray or application specific integrated circuit or a microcontrollerspecifically configured to implement one or more aspect of the adaptivereporting process 200 described in relation to FIG. 2. In general, thereporting may be provided (displayed) via a user interface (e.g., humandetectable interface), such as a continuous monitoring device, a patientrecord system, a smartphone, or a social media internet site.

For ease of explanation, FIG. 2 will be described with reference toglucose monitoring. The adaptive reporting process 200 begins at block202 where glucose information is reported to user/patient in a firstmanner or style. Not all users/patients intuitively prefer, or are ableto cognitively handle, a common reporting style. Furthermore, not allusers/patients may be situated to receive the same report at all times.For example, a younger patient who regularly interacts with electronicdevices may adapt differently to the continuous monitoring system thanan older patient who rarely interacts with electronic devices. Asanother example, if a user/patient is driving, it may be desirable toprovide a concise report of information that can be comprehended whileoperating a car. Similarly, if the user/patient is at home watchingtelevision, a more robust report may be desirable. Such variations mayaffect patient behavior and impact utilization of the device and/oroutcomes from using the device.

In some implementations, the first manner or style may be predeterminedsuch as during device manufacture time. The first manner or style may beselected so as to provide the basic information. In someimplementations, a learning phase may be selected for the device. Duringthe learning phase, the device may utilize a default first manner orstyle. In some learning phases, it may be desirable to avoid reportingor to provide a limited report on some interactions/events until afterexpiration of the learning phase. The duration of the learning phase maybe based on the quantity of input information before deciding to turn on(or prompt) a feature at all. For example, a new device may not haveinformation regarding the patient. The learning phase may receiveinformation about the patient such as physiological readings from theglucose sensor, activity information (e.g., pedometer information),location information (e.g., GPS coordinates), and the like. Oncesufficient data to begin the adaptation process is obtained, thelearning phase may terminate.

A style or manner of reporting may include audio feedbackcharacteristics such as frequency, tone, and volume of an alarm. Thestyle or manner of reporting may include visual characteristics such asresolution of a trend graph, brightness intensity, colors for graphs,interface iconography, interface symbology (e.g., for alerts),magnification level of displayed information, and the like. The style ormanner of reporting may include information display characteristics suchas the orientation of trend graphs, trend graph ranges, graph colorscheme, dynamic trend graphs, and the like. The style or manner ofreporting may include a reporting frequency, a number of alerts of aparticular type (e.g., actionable alerts, informational alerts, etc.),an amount of avatar help (e.g., quantity of interactive automatedassistance via an animated character display), a vibration intensityand/or frequency, amount of data to display (e.g., past hour, 2 hours,etc.).

Other reporting related styles or manners that may be provided (andadapted over time) include a default display for the report, levels ofdiscretion for reporting based on context, input configuration for areport (e.g., hitting a key on the device while viewing a trend graphtemporarily magnifies trend graph to trend line range or to magnifyaround current glucose level/trend), glucose acceleration information(e.g., your glucose speed is still going up, but not as quickly—a signthat insulin is starting to act), prediction mode time, future modetime/inputs (e.g., based on glucose, insulin, exercise, and other inputinformation that may be available, a prediction of future mode time orinputs may be generated. In some implementations, the prediction may bebased on previous user data whereby similar past patterns of inputs canbe used to improve estimate).

Alert and/or alarm settings that may be provided (and adapted over time)include one of multiple high level contextual categories in someimplementations. For instance, there could be three categories:information only, safety mode, and attentive mode. In the informationonly configuration, the system turns off all alarms and alerts and onlydisplays the information. In safety mode, the system sets alarms atsevere hypo and hyperglycemia. In attentive mode, the system isconfigured to operate under tighter glycemic control goals (i.e., 80 to150 mg/dl). These settings would be the defaults but the system may,over time, adapt each individual setting (e.g., alert level, volume,etc.). The modes may also be applied selectively by the system based oncontext. For example, while in a meeting at work, the information onlymode may be desirable.

The style and/or manner may include order of screens or menu items orhide items, which may be adapted over time. The style or manner may alsoinclude a frequency and/or content of any information published to otherdevices or to other people such as a doctor or caregivers, which mayalso be adapted over time.

At block 204, as shown in FIG. 2, behavioral and/or contextualinformation about the user/patient is determined. FIG. 4 provides moredetail on how behavior and/or contextual inputs may be captured,tracked, determined and indicated, any of which may be applied as asubroutine at block 204 of the adaptive reporting process 200. Byidentifying certain contexts or behaviors during natural use of thedevice, the system can adapt the reporting style for the context orbehavior of that user. For example, the system may identify when apatient is frustrated or confused by detecting the patient staring atthe screen for a long period of time via eyeball tracking or other imageor haptic based detection. As another example, the patient may beidentified as satisfied based on facial pattern recognition informationobtained from image data. As a further example, the system may determinethe patient is in a certain social situation such as in a work meetingor out to dinner with friends based on GPS and/or calendar information.As yet another example, the system may determine the patient isexercising or driving via input from an accelerometer, calendar, and/orGPS). In each of these examples, the context and behavior can beassociated with the physiological information without actually requiringthe user to input the determined information.

Context and behavior information can also be obtained via user input.For example the user may be prompted to indirectly guide or adjust thebehavior of the system by asking them to answer weighted questions. Forexample, “On a scale of 1 to 5, do you prefer people knowing everythingabout you”, “How often do you check your friends Facebook pages”, “Howoften do you check the time or temperature”. Each question can berelated to one or more underlying reporting style or manner. Forexample, the questions regarding knowing everything or Facebook may beused to establish a frequency and quantity of data to report to others.

While some implementations may achieve a more accurate adaptation usingindirect questions, in some implementations the system may be configuredto directly receive weighted preferences for various reporting styles ormanners. For example, a slider interface control may be calibrated from“very little” to “as much as possible” for a “how much feedback do youwant”, “how much information do you want to share with friends ordoctor”, etc. These direct questions can impact how often, if at all, aspecific alarm is triggered. Note, however, the user/patient is notdirectly asked whether to turn off the alarm in this implementation.

Determination of behavioral and/or contextual information about theuser/patient will be discussed in further detail below, such as inreference to FIG. 4. Namely, contextual and or behavioral inputs may becaptured as described at block 402; the behavior and/or contextualinputs may be tracked over time to collect a database of information asdescribed at block 404; and the inputs may be processed to determinebehavioral and/or contextual information about the patient as describedat block 406, based on which an indication of the contextual and/orbehavioral information may be optionally provide as described atoptional block 408 and/or the contextual and/or behavioral informationdirectly inputted to block 204 described herein.

At block 206, the behavioral and/or contextual information is comparedto a goal/criterion. The goal/criteria may be an adaptive goal or apredetermined goal as will be described in further detail in FIG. 5(see, e.g., block 502 and 510). Sometimes a patient's behavior and/orcontext will limit their ability to gain full benefit from thephysiological information, by setting goals that take into accountbehavior and/or context, the useful physiological information can beindividualized to meet a user's needs without any effort on their part.

Exemplary goals include an amount of: interaction with the device,amount of time in target, amount of time outside of target, devicelocation (e.g., not leaving device behind), data retention, calibratingfrequency, standard deviation, pattern management (e.g., times of dayin/out of target), time spent on certain screens, time spent hypo, timespent hyper, time spent at high rates of change, and time spent at lowrates of change. Criteria may be set to determine whether the user hasmet the goals, for example, criteria may interaction with the monitoringsystem at least 10 times per day, at least 22 hours per day withintarget, no more than 2 hours per day outside of target, amount of datacaptured, number of fingersticks entered, number of menu selectionsand/or button clicks, etc. In general, it should be understood thatgoals may be used to define by criteria (both of which may be adaptivelymodified over time as described with respect to FIGS. 5 and 6), whichmay be compared with behavioral and/or contextual information determinedat block 204.

In some exemplary embodiments GPS input information may be used by thesystem to determine a user went for a walk in the hills, which may becompared with a goal/criterion. Input information from an accelerometermay be used by the system to determine they exercised more or they sleptmore, which may be compared with a goal/criterion. Input from theInternet of Things (IOT) (e.g., machine to machine communication) may beused by the system to determine if the user watched less TV or boughtmore healthy food for the refrigerator, which may be compared with agoal/criterion. Blood pressure data obtained from a blood pressuresensor, input manually, or retrieved from memory (e.g., patient carerecord) can be used to acknowledge the associated user managed stressbetter, which may be compared with a goal/criterion. Goals will bedescribed in further detail with reference to FIG. 5.

The incorporation of machine to machine communication provides severalnon-limiting advantages to the effective treatment of a patient with acontinuously monitored condition, wherein data obtained from machine tomachine communication may be compared to goals/criterion. The device cancommunicate with other networked devices thereby expanding the type ofdata which may be provided to the system and collected. For example, themonitoring device may be in data communication with various devices inthe patient's home such as a television, refrigerator, temperaturecontrol system, computer, gaming console, security system, and the like.The monitoring device may be configured to automatically discover theclosest networked devices that can provide data (wherein thegoal/criteria is based on nearness and the context information includesnearby devices, including their location/nearness). In someimplementations, the monitoring device may be configured to connect witha pre-determined set of devices. In such implementations, additionalsecurity credentials may be provided to control access to the data. Astwo examples, the data communication may be via a central network (e.g.,local area home network) or via a peer-to-peer mesh network.

Consider the following implementation including machine to machinecontext information. The networked sensor (e.g., a GPS capable device)can provide input to the system for determining when the user of themonitoring device is at home (i.e., context information). The GPS may bea standalone navigation unit, included in the monitoring device, orincluded in another device configured to provide the locationinformation such as a smartphone. Once at home, the light bulbs may beconnected to the network (i.e., criteria for using light bulbs includesGPS location indicating the patient is home). The light bulbs may beused to provide reports to the user while at home. For example, a reportmay include transmitting a signal to the light bulb to change color whenthe user is high or low at home (e.g., turns purple for high and orangefor low). If the system received inputs which indicate that the user'sanalyte level is below a threshold and is not acknowledging alarms, viathe Internet of Things (IOT), a hierarchy of alarms can be transmitted.First, text messages are sent to pre-determined followers that arenearby (known by their GPS locations). Followers may be notified basedon their proximity to the person (e.g., no need to notify a person whois determined to be located in different state). The front door lockopens so nearby followers can get into users house (see, for example,Lockitron a networked door lock manufactured by Apigy Inc.). If nearbyfollowers are not available, an email or text message may be issued toemergency services. Front door light (also networked) can change topink, allowing emergency services to quickly identify which house theyneed to look for.

Followers may include all members of a community of users. For example,a follower network of all DexCom users can be created whereby each useris identified as a follower of another person in the DexCom network whois nearby. In this way, strangers can help strangers if nearby. Such asocial system can assign points for helping strangers out and may attainan elevated status in the diabetes community. These interactions canfurther facilitate awareness and help save lives. The follower networkmay be configured based on privacy preferences or other reportingcriteria (e.g., forms of reporting to use via the network, amount ofdata to transmit, etc.).

As described thus far, the monitoring device provides messages to thedevices via the machine to machine communication. In someimplementations, communication may occur in the reverse direction, suchas a severe low glucose with no signs of movement may activate a phonespeaker which permits playback of sound (e.g., a phone call) via themonitoring device to anyone nearby who might be able to help.

At block 208, the manner, in which the information derived from thecontinuous monitor is reported, is adjusted. The adjustment may be basedon the comparison of block 206. For example, improvements in userinteraction/displays/alarms, etc. could be identified by the system.Such identification may be preemptive and adaptive based on patterns.Some exemplary reporting manners and styles that may be adjusted aredescribed with referenced to block 202, however, it should be understoodthat any manner or style (characteristic or feature) of reportinginformation may be adjusted in any way as is appreciated by one skilledin the art. The amount of iterative and/or total adjustment of reportingstyles may be limited by boundaries predefined by the manufacturerand/or set by a user (based on human factors studies, for example). Byiteratively trying new reporting styles and watching how the user'sbehavior responds or their ability to respond (e.g., would not respondwhen in a certain context, such as a meeting) and/or contextuallyapplies differently, the reporting style can be individualized for apatient and adapted over time as a change with the changing environment,behavior, and/or context of the patient. The adjustment in someimplementation is performed automatically. In some implementations, theadjustment may be performed via a recommendation whereby one or morepossible adjustments are provided and an indication of one or moreadjustments to apply is received.

The adjustment to the style or manner of reporting may include audiofeedback characteristics such as changing frequency, tone, and volume ofan alarm (e.g., increasing, decreasing or other changes). The adjustmentto the style or manner of reporting may include changing visualcharacteristics such as resolution of a trend graph, brightnessintensity, colors for graphs, interface iconography, interface symbology(e.g., for alerts), magnification level of displayed information, andthe like. The adjustment to the style or manner of reporting may includechanging information display characteristics such as the orientation oftrend graphs, trend graph ranges, graph color scheme, dynamic trendgraphs, and the like. The adjustment to the style or manner of reportingmay include changing a reporting frequency, a number of alerts of aparticular type (e.g., actionable alerts, informational alerts, etc.),an amount or type of avatar help (e.g., quantity of interactiveautomated assistance via an animated character display), a vibrationintensity and/or frequency, amount of data to display (e.g., past hour,2 hours, etc.). The adjustment to the style or manner that may include adefault display for the report, a level of discretion for reportingbased on context, an input configuration for a report (e.g., hitting akey on the device while viewing a trend graph temporarily magnifiestrend graph to trend line range or to magnify around current glucoselevel/trend), glucose acceleration information (e.g., your glucose speedis still going up, but not as quickly—a sign that insulin is starting toact), a prediction mode time (e.g., longer or shorter predictionhorizon), a future mode time/input (e.g., based on glucose, insulin,exercise, and other input information that may be available, aprediction of future mode time or inputs may be generated. In someimplementations, the prediction may be based on previous user datawhereby similar past patterns of inputs can be used to improveestimate). Alert and/or alarm settings may be adjusted, such as one ofmultiple high level contextual categories, wherein the system may, overtime, adjust the high level contextual categories and/or adapt eachindividual category setting (e.g., alert level, volume, etc.). The modesmay also be applied selectively by the system based on context. Forexample, while in a meeting at work, the information only mode may bedesirable. As another example, the style and/or manner adjustment mayinclude reorder of screens or menu items or hide items. The adjustmentto the style or manner may also include changing a frequency and/orcontent of any information published to other devices or to other peoplesuch as a doctor or caregivers, which may also be adapted over time.

As another example adjustment, the received information may indicatethat a response is not received at night unless the alarm is provided ata certain volume. In this situation, the patient may be a deep sleeperand the default alarm volume during periods of sleep needs to be louder.As another example, the patient's spouse may be identified as a lightsleeper (context) so alarms should be vibration only (adjustment) untila certain glucose reading (e.g., LOW 55) is hit. As another contextadaptation, or when the patient is identified as going for a walk(behavior/context), the device may determine that a user wants a“diabetes vacation” for just 30 minutes and does not want to be botheredunless LOW 55 may be hit within the hour (adaptation). Environmentaladjustments may also be provided such as if the user is determined to beoutside most of the time (context) a brighter contrast may be used for atrend screen display. Other adjustments can include providing higherlevel of discretion (adjustment) when detecting the location a workmeeting (context), providing a basic, high-level report to a userdetermined to be overwhelmed with data or to a newly diagnosed user. Insome implementations, the adjustments may feature a graduation schemewhereby upon completion of certain criteria, the reporting style isadapted to a next-level of sophistication. The adjustments may provide aset of adaptations that are continuously applied (once adjusted, untilthe next adjustment) and/or that are applied depending on the behavioror context identified at any particular time (a profile of adjustmentsthat depend on real-time context/behavior).

A further example of an adjustment includes altering the iconographyand/or alert symbols that reflect real time data. For example, when alow alarm goes off, the icon on the device for “low alarm” could show animage of the trend graph going low with actual data points from glucosereadings instead of a generic representation of a low glucose chart.

Organizing the adapted reporting may include identifying a hierarchy ofreporting beginning with basic reporting and escalating to a morecritical/complex report. For example, in the case of the deep sleepingpatient, sensor inputs may indicate that the user is not acknowledginglow glucose alarms triggered during the night. In a diabetic situation,this can be caused by the person sleeping or, more critically, that theperson has entered a dangerously low state and is unresponsive. Thereporting may be adapted to provide an initial loud alarm during eveninghours and progressively increase volume over a period of time. Thehierarchy may also include a threshold point whereby alternate reportingmeans are used to provide the alert such as a phone call to the user'shome, a phone call to neighbor, and, perhaps the ultimate alert beingdefined as a phone call to emergency services (e.g., 911).

Based at least in part on the collected context and behavior informationin conjunction with the monitored physiological characteristics, thesystem is configured to assess how a patient's condition behaves andresponds over time. For example, the weather (context) combined with apatient's exercise regime (behavior) could cause glucose lows. Thepatient may not identify this correlation, but through analysis of thecollected information, a prediction that the low may occur based on thepast events. In such implementations, the monitor may take proactivesteps to maintain the patient's levels such as administering medication,increasing the frequency of monitoring, displaying a warning message, orthe like.

Analysis of the specific characteristics of the patient's condition canbe useful in identifying other trends or anomalies with the specificpatient. For example, if atypical patterns are detected, other issuesmay be assessed as potential causes of the variations. Such issues mayinclude food allergies, celiac disease, gastroparesis, etc., which maybe identified based on the context/behavior information. This pastknowledge informs guidance/advice to best respond to certain situationsor trends. For example, a potentially rebound hypo may be detected asoccurring on a regular basis. The system can be adapted to automaticallyalert the patient at a time they should eat to best avoid bothhypo(glycemia) and a rebound hyper(glycemia). Trends may also beclassified in the moment based on hypo/hyper risk, rebound, etc. andfrequency of reporting adapted accordingly as risk of a glucoseexcursion changes.

The changes to reporting characteristic need not be in the same categoryas the behavioral information. For example, while increased userinteraction could trigger a change in the interface, increasedhypoglycemia could also trigger a change in the interface. In someimplementations, one type of behavioral information can be used toadjust multiple reporting characteristics. Similarly, multiple elementsof behavioral information may be used to adjust a single reportingcharacteristic.

The context/behavior information may be used to configure general devicefeatures. For example, the general device user interface may bereconfigured, graph, display of sensor data, buttons, alarms, defaultscreens, and preferences for interaction. In some implementations, itmay be desirable to interact with the user via a game. In such a mode,rewards may be provided when desired behaviors are detected. In suchimplementations, the type of rewards which are provided, amount ofreward, and frequency of rewards given may be adjusted based on thecontext/behavior information.

Discretion may be desired for certain users. As one way to ensurediscretion, first level alarms may be provided which only vibratewithout turning on the screen or otherwise “awaken” monitoring devicefeatures. In such implementations, the monitoring device may awaken whena button press is detected. The button press may include a definedseries of buttons which permit inadvertent awakening through, forexample, a “pocket push.” This may further provide a level of securityto the device by allowing a custom awake input to be identified for themonitoring device.

At block 210, the report is provided based on the identifiedadjustments. As discussed, altering the reporting of glucose informationbased on personalized interaction can help increase usage, response toprovided information and alerts, and ultimately improve outcomes (e.g.,health). The user's behavior and/or context can continue to be trackedand their response determined. Additional adjustments may be identifiedthrough subsequent iterations of the adaptive reporting process 200. Theadjustments to the reporting format and/or goals/criteria are used foradapting the reporting format so that interaction with the physiologicalinformation is personalized for a particular person, in a particularsituation, at a particular time.

How the report is provided is a further adaptation which may beperformed by the system based on the obtained contextual and/orbehavioral information. For example, a report may be intended foranother device (e.g., machine to machine device), or for humanconsumption (e.g., email, text information). Example report destinationsinclude an insulin pump, a networked storage device (e.g., cloudstorage), a social media community, a Smart TV, a computer or anapplication running on a computer (e.g., widget), a phone, a watch,doctors or other care providers, to PCS (patient customer support) team,parent, loved one, a follower (e.g., a person with whom a continuousglucose monitoring (CGM) patient is “sharing” their CGM data), anactivity tracker, a cell phone, a ring, a pendant, smart refrigerator, amedical system like hospital equipment or hospital network, gaming orapplications on phone where rewards are included as game points/currency(e.g., credits that may be used in-game to acquire additional features),and the like.

Providing the report at block 210 may include providing positivefeedback to the user. A determination of how the patient responds to thepositive feedback (e.g., more success with positive feedback as comparedto less or no positive feedback) could be tracked as a patient input forcontext/behavior information. The system may be configured to use thisinformation to adapt goals and/or further resulting reporting formatssuch as described with reference to block 208. The feedback may beorganized at an individual patient level. The feedback may be providedto a group or team of users so that the incentive is relationship orteam-based/connection or partnering with others.

Devices which include such feedback can improve confidence in diabeticsby positive reinforcement. Providing the report at block 210 may includeidentifying positive trends or behaviors and providing this informationin a report. For example, simple goals may be associated with a user,such as to wear the sensor for two days or to check their monitorednumber five times in a day. Such simple goals may be organized into ahierarchy such that easier goals must be accomplished first before“graduating” to next level of complexity.

The system may identify a pattern of positive events, such as animprovement or a good monitoring report, and provide a reward. Thereward may be known by the patient a priori or be a surprise reward. Thereward may include a “bragging” reward tied to social media such as alimited access avatar, icon, or the like.

Providing the report may further include indication of goals andachievement thereof. Goals can be user defined (e.g., by patient,doctor, parent) or predetermined (e.g., library of goals for diabetespatient). User gets rewards and badges for achieving these goals. Viabadges, user advances through the ranks of CGM knowledge, and moves tothe next step, thus reinforcing success. The information collectedregarding goal achievement may provide data as to which interventionsare more effective than others.

Other examples of feedback which may be provided at block 210 includeacknowledgement of a period of time when the system does not detect atarget analyte level below a threshold. Such periods of time may bereferred to as “no hitter days.” Other example forms of feedback arepraise for going to a doctor appointment, or scheduling of otherappointments to help manage the full picture of living with diabetes(e.g., eye doctor, dentist, etc.). On a day when the glucose control didnot meet the target criteria/threshold feedback may be provided toencourage or motivate the patient. Such days may be referred to as “bad”days. The target criteria or threshold for “no hitter days” or “baddays” may be preset, default, user defined, or adapted over time). Anexample of feedback for a “bad” day may include selection by the systemof a message which offers hope (e.g., it's ok, everyone has an off day).In some implementations, the message may include patient specificinformation such as, “Remember X day when you had a no hitter? Tomorrowis a new day with a fresh start, get some rest and try again!” In thisway, the patient can be reminded that not all experiences with diabetesare positive, but regardless of hard efforts you have to wake up and dothe same thing with the same constant effort all over again so it'simportant to improve confidence by acknowledging that “bad” days happenbut there will be/can be good days ahead.

Based on the initial processing, additional information or inputs may beidentified as being useful to further adaptation. For example, if nolocation information was identified during the adaptive reportingprocess 200, at block 210, a request to enable the GPS or provide a GPSinput may be included. For inputs which were received, configuration ofthe sensors providing the inputs may be assessed and recommended changesmay be suggested. For example, the sensor insertion location can affectthe monitoring process. Upon identifying variance in the data receivedfrom the inserted sensors, adjustments to the sensors may be suggested.

As one example implementation, after providing the report (block 202),the system determines the user has not calibrated the device in severaldays. This determination is based on received data from the device whichis stored each time the device is calibrated. The date of the lastcalibration may be stored in a memory and compared to the current date(to determine behavioral information at block 204). If the number ofdays exceeds a threshold, the absence of calibration may be identified(by comparing behavior information to goal/criteria at block 206). Asthe accuracy of the report may be affected by the calibration, uponidentifying this behavior, the system may adapt the report to includefeedback or other content (at block 208). The feedback may be identifiedfrom a catalog of feedback items categorized by behavior and/or context.The system may identify a static item (e.g., textual message regardingthe importance of calibration) for inclusion within the report. Thesystem may identify a dynamic item. The dynamic item may include userspecific information such as the number of days since last calibration.The dynamically included information may be selected based on thereceived behavior and/or contextual information for the user. The systemmay identify an interactive item. An interactive item is one whichincludes a prompt for the user to provide a response. The response maybe free-form (e.g., open ended question with text information providedin response), or limited form (e.g., multiple choice, sliding scale,validated values). Once included in the report (at block 210), thefeedback may be presented within the report or via a messaging interfaceincluded within the device (e.g., notification icon, text message,email). If the feedback includes an interactive item, the devicereceives the user input and transmits the response to the system forfurther processing. The further processing may include storing theresponse, natural language extraction of keywords from the response,identifying a subsequent feedback item, and the like.

Having described a method of adjusting an interface format/style forphysiological information based on behavioral and/or contextualinformation in FIG. 2, certain example embodiments incorporating thefeatures described may serve to further highlight the innovative aspectsof the method.

In another example, the monitor reports CGM data to user with highsensitivity alarms (at block 202). The system determines that the usershows a pattern of ignoring alarms (behavior determined at block 204).The determination is based on an analysis of frequency of interactionwith the CGM display and alarm state lasting greater than 1 hour onaverage. The system may identify a goal for this user from a library ofpre-determined goals based on the identified behavior pattern orcontextual information. The goal may be to experience alarm stateslasting no more than an hour on average (pattern) and/or interactionwith the device at least twice during an alarm state lasting more thanan hour (block 206). To help achieve this goal, the system may furtheridentify adaptations to the reporting style such as changing tone,increasing volume and/or increasing frequency of alarms (block 208).With the adapted alarming format, the user continues using the monitorwithout necessarily having to perform the analysis and adjustmentidentified by the system (block 210). In some implementations, theadaptation may be confirmed through a received user input (e.g., activea control acknowledging the change).

Further details about alarms such as setting alarms, alarm states,criteria for alarms may be found in U.S. patent application Ser. No.13/742,694 filed January 16, and entitled “Systems and Methods forProviding Sensitive and Specific Alarms,” the entire disclosure of whichis hereby expressly incorporated by reference. The alarms, states,and/or criteria described may be adapted using the systems and methodsdiscussed herein.

In another example, the monitor provides personalized assistance inmaintaining analyte levels within a target range. The system recognizesthat the user only uses a certain interface of the system (afterreporting at block 202, behavior is determined based on screen views atblock 204 and compared to a criterion at block 206). The systemautomatically adapts the menu/screen prioritization to a predeterminedscheme associated with users under good glucose control (e.g., averagereading over a period of time within a threshold amount) at block 208.The adaptation (208) may include storing a flag indicating theparticular menu item is to be hidden (210). The adaptation (208) mayinclude adjusting a list of menu items used to generate the display suchthat the adjusted list features the items more frequently used near thetop of the list and less frequently used items lower in the list (210).If the user is not under good glucose control the system may be adapted(at 208) to provide (at 21) tips on other interfaces which may be usedto increase control. The tips may include information messages,advertisements for other products, or additional behavioral/contextualinputs which may be useful to incorporate into the monitoring system.

Where advice/guidance is provided, the provided information may be basedon known best practices to correct/avoid devolving trend or riskierglucose excursion. The information can be selected and/or reference pastdata of user or similar population as well. The provided information caninclude multimedia information, textual information, or audioinformation. The information may be stored in a database and associatedwith one or more context, behavior, or monitored conditions.

In some exemplary implementations, the system may determine the behavior(at 204) that a patient is not calibrating at suggested intervals (e.g.,every 12 hours) at 206. In some cases, it may be determined that thepatient is skipping calibrations completely. To decrease the amount oftime CGM is prompting for calibrations, the reporting for this user isadapted (at 208) to provide a message after certain number of missedcalibrations that monitored values may not be accurate due to systembeing out of calibration.

In some exemplary implementations, the system may determine that thepatient does not set a high alert or low alert. One of the advantages ofcontinuous monitoring is to have the patient make changes to theirbehavior in light of past trends. For such a patient, the monitor candetermine if person is spending too much time in high or low ranges. Themonitor may then be adapted to provide a report (e.g., once every 3-4days) letting the patient know that blood glucose is extremely high(e.g., 400 mg/dL) or running on the low side (e.g., 60 mg/dL) at certaintimes of the day. This is just one example of identification of an alertand adaptation. The system may be configured to identify a variety ofother patterns and apply one or more adaptations accordingly.

In some exemplary implementations, the system may provide a set ofpredetermined reporting profiles. A given user may be associated withone of the profiles based on the received context/behavior information(e.g., a user's preference on how the system should behave). Forinstance, an extra anxious user may like to alerts that are frequent,and sounds that are soothing because he wants to know where he is allthe time, but at the same time does not want loud alerts. If the user isnot responding the alerts or looking at the screen too often, the systemmay determine that behavior and change the alert settings accordingly(i.e., changing thresholds from 80 to 70 or vice versa). As anotherexample, if the user is changing from normal to silent every morning ata specific time, the system can adapt to perform such adjustmentautomatically based on the historic information for the user.

In some exemplary implementations, the system may adapt based on adetermination of how long it typically takes a particular patient to gofrom 100 mg/dL to 60 mg/dL or 200 mg/dL or 300 mg/dL based on time ofday, day of week, or using other external sensor inputs. This predictionof risk estimation may be based on knowledge of insulin, exercise, andtime of day/week. The goal of risk estimation is to determine if anindividual is at risk (or has higher odds) of becoming hypoglycemic on aparticular night. Whether one becomes hypoglycemia depends on a numberof parameters such as, food, exercise in the past 24/48 hours, insulinon board, hormonal changes, and stress.

To perform the adaptation, a probability distribution of theseparameters may be provided for an individual. For example, probabilitydistributions for hypoglycemia based on information of one or more ofthe following: food, exercise, insulin on board, hormonal changes, andstress may be provided a priori and/or adapted for a particular patientbased on patterns over time. Probability distributions may be providedfor other parameters/information as well. A ‘likelihood’ estimate maythen be generated by the system based on the probability distributionsto determine if a person has less or more likelihood of becominghypoglycemia. For example, if the person is normal and all hisparameters/information described above are around their mean values(e.g., within the distribution), then the likelihood of being normal(which is a product of all probabilities) is high. If one or moreparameters deviate from the mean, then the product deviates from thelikelihood of being normal.

In one exemplary implementation, the parameters include:

1. Food consumed in the past 4 hours: N(mu1, sd1), with peak Probabilityof 0.1 at when food consumed is at mu1(e.g., 100 g of carbs).

2. Insulin on board in past 8 hours: N(mu2, sd2), with peak Probabilityof 0.1 at when insulin at mu2 (e.g., 20 Units).

3. Exercise in the past 4 hours: N(mu3, sd3), with peak Probability of0.1 at when the exercise is at mu3, (e.g., 2 hours intensive).

Assume for now these parameters are normally distributed with mean andstandard deviation as noted. These values and probabilities are thenormal levels for this person at which he is considered normal (i.e.,his glucose is within the normal range, say 70-180 mg/dL). On aparticular day, the likelihood of observing data when this person isnormal is defined in Equation 1 and based on the example a parametersabove.

L(data/normal)=Prob(food)*Prob(Insulin)*Prob(exercise)   (1)

If everything is at the mean (i.e., normal), the likelihood is highest,which in this example is 0.1*0.1*0.1=0.001. Suppose the systemdetermines that the person ate a lot less food than normal on aparticular day (e.g., 10 g of carbs). In this case, the probabilityvalue for lOg in his distribution is value much smaller than 0.1. Forexample, this can be 0.01. When this happens and if he did the sameamount of exercise and took the same insulin, the likelihood of hisbeing normal will now be 10 times lower or alternatively he is 10 timesmore likely to become hypoglycemia.

As described, the system adapts the model to a person's normal behavior.When inputs indicate a change, the system generates an estimate of howthat change impacts the risk of becoming hypoglycemic. For the food forexample, mu1 is the typical carbs one eats in a meal (e.g., 50 g) andsd1 would be the variation from meal to meal (i.e., sometimes one eats45 g, sometimes 55 g, etc.).

FIG. 3 shows a plot of the histogram (or distribution) of carbs in ameal for an individual over a period of 30 days. The histogram shownincludes a mean (mu1) and standard deviation of (sd1). The informationfor the graph may be used to adapt the reporting described above. Forexample, alert glucose threshold may be adjusted if it is determined thepatient is located in an Italian restaurant.

Another example adaptation is to adjust the menu items based on theirhelpfulness to a patient in managing their disease. For example, thesystem detects how often a user accesses a particular menu item. As usedherein, a menu item generally refers to a display selection or userinterface control which provides information to a user of the device.Menu items may be fixed based on what the system developer believes isthe frequency of access. However, as noted above, perception of usefulitems can vary from patient to patient and the determined format for themenus may not be useful for disease management of all people in the sameway. Accordingly, the system can identify a frequency of access for eachmenu item. Based on the identified frequency, the presentation of themenu items may be adjusted. Adjustment may include re-ordering menuitems, hiding menu items, increasing the presentation to be more or lessprominent (e.g., font size), associating the menu item with a “hot-key”or the like. By the described features, the priority and presentationcharacteristics of menu items may be adapted.

As thus described, the system includes a set of reporting profiles thatdefines the behavior of alerts. These defined profiles are based onpreviously obtained information regarding different profiles that matchpeoples' preferences on average. Upon observing the actual behavior of aparticular user when the user starts using the system, the system canadapt the profile to match the particulars of the user. The adaptationmay include suggesting a new profile if the user specified a preferencefor a profile, but selected a profile which is not suited to theidentified context/behavior detected by the system. The system can thenapply the new, adapted profile that matches the user better.

FIG. 4 is a process flow diagram of a method of determining behavioraland/or contextual information for a patient. It may be desirable tocapture inputs without the user having extra efforts/interactions. Byconfiguring the system to capture behavior and/or contextual informationfrom various inputs associated with user behavior and/or contextsurrounding use of the device, without burdening user to explicitlyprovide such information, the user will be more likely to use andbenefit from the device. For example, a user can walk past acommunication hub that has been installed in their house or car thatwill sync up the data inputs such as to the cloud/server. The system maybe configured to organize the data and learn and organize theindividual's behaviors. Goals or criteria may be identified based on thedata. The information may be transmitted back to the device atuser-specified intervals, daily, weekly, monthly, etc.

The process 400 shown in FIG. 4 may be implemented in whole or in partusing a continuous monitoring system such as the devices shown anddescribed in FIG. 1. The behavioral and/or contextual informationdetermination process 400 may be implemented as a server process in datacommunication with a continuous monitoring device. The behavioral and/orcontextual information determination process 400 may be implemented inhardware such as via a field programmable gate array or applicationspecific integrated circuit or a microcontroller specifically configuredto implement one or more aspect of the behavioral and/or contextualinformation determination process 400 described in FIG. 4.

The process of determining behavioral and/or contextual informationuniquely identifies information useful for solving a long felt need ofpersonalizing general purpose devices for healthcare use, or health caredevices intended for use with a wide variety of user preferences, in asimple and intuitive manner. That is, not all users have the samepreferences when it comes to personalization of a medical device,especially a consumer-driven medical device. Some users are tech savvyand enjoy reviewing large amounts of their health data; other usersprefer a simpler interaction. Many fall in between and their preferencesmay be influenced by the context surrounding their interaction.Unfortunately, creating devices that are highly customizable also tendto be highly complex, and vice versa, therefore do not address the fullspectrum of users. There remains a need for automatically and adaptivelyunderstanding the behavior and context of the user of a consumer-drivenmedical device that is highly intelligent and allows for simplicity ofuse. In one implementation, these needs are met by capturing contextualand/or behavioral inputs (402); tracking the behavior and/or contextualinputs over time to collect a database of information (404); processingthe inputs to determine behavioral and/or contextual information aboutthe patient (406); optionally providing an indication of the contextualand/or behavioral information (408) and/or optionally providing thebehavioral and/or contextual information to any of the adaptiveprocesses described herein (i.e., block 204 of process 200; block 504 ofprocess 500 or block 602 or process 600), a device may be efficiently,intuitively and intelligently personalized for optimizing health caremanagement and use, without requiring a complex or comprehensiveunderstanding of technology, human behavior (or context) and health datathat would otherwise extremely difficult, inefficient and likelyimpossible for a human to perform as is made possible by the systems andmethods described herein.

At block 402, contextual and or behavioral inputs are captured. Theinputs may be discovered or registered as described, for example above.Behavior input information may be obtained via the system. Behaviorinputs can include an amount of interaction, glucose alert/alarm states,sensor data, number of screen hits, alarm analysis, events (e.g.,characteristic associated with the user's response, time to response,glycemic control associated with the response, user feedback associatedwith the alarm, not acknowledging alerts/alarms within x minutes, timeto acknowledgment of alarms/alerts, time of alert state), diabetesmanagement data (e.g., CGM data, insulin pump data, insulin sensitivity,patterns, activity data, caloric data), free fatty acids, heart rateduring exercise, IgG-anti gliadin, stress levels (sweat/perspiration)from skin patch sensor, free amino acids, troponin, ketones, ketones,adipanectin, troponin, perspiration, body temperature, and the like. Theinputs may be provided by a sensor in data communication with themonitoring device. In some implementations, the information may beobtained through an intermediary such as a remote data storage.

Contextual information which may be provided as an input to the systemincludes a person's biology, location, sensing surroundings (e.g.,light, sound level), environmental data (e.g., weather, temperature,humidity, barometric pressure). The inputs may be received viapeer-to-peer or mesh network via machine to machine communication asdiscussed above. Context information can include daily routineinformation (may change especially from weekdays to weekends) fromcalendaring application. Context information can include frequency oftouching or grabbing the monitoring device, even if not interacted with,based on sensed motion of the device (e.g., accelerometer or camerasensor). Photos can provide contextual information. For example, photosof one or more of: a glucose meter reading, an insulin pen or pump JOB,a location (e.g., gym, park, house, Italian restaurant), or a meal maybe used to provide context information. The photos may be processed toidentify, for example, caloric intake for the meal shown in the photo.The type of insulin used may also be provided to the monitoring systemas an input useful to adapt the system. Context may also be provided bybasal or bolus settings provided to or determined by the monitoringdevice.

Other inputs to the adaptation process include exercise information froma fit bike or the like, glucose sensor information from a blood glucose(BG) meter or CGM, insulin delivery amounts from insulin deliverydevice, insulin on board calculation for the device, and other deviceprovided or calculated information.

Hydration level, heart rate, target heart rate, internal temperature,outside temperature, outside humidity, analytes in the body, hydrationinputs, power output (cycling), perspiration rate, cadence, adrenalinlevel, stress, sickness/illness, metabolic/caloric burn rate, fatbreakdown rate, current weight, BMI, desired weight, target calories perday (consume), target calories per day (expend), location, favoritefoods, level of exertion are additional examples of context/behaviorinputs to the adaptive process.

For any of the above referenced behavior or contextual inputs, thesystem may be configured to receive and/or generate analytical metricsbased on the inputs. For example, a composite value may be generatedbased on the glucose level, temperature, and time of day to generate anindex value for the user.

As the system may provide goals, behavior information can includedetected improvements or success rate of goals/criteria. It has beenshown that people who look at historical glucose data have betterdiabetes management. The information can include time stamp to identifyhow often people look at their historical glucose data.

The initial inputs at block 402 may be internally (e.g., within themonitoring device) derived data such as sensor measurements, internalcalculations and/or other information provided by the patient or knownby the system at the initial query stage. For example, information aboutuser interactions and outcomes (short term and/or long term) of thatresponse may be identified.

The initial inputs may be used to generate a degree of confidence foradaptations/advice/guidance generated by the system for user. The degreeof confidence can allow the user to specify a level of “fuzziness” inthe adaptation, advice, guidance they are willing to accept. One way theinitial input may be used by the system to generate a degree ofconfidence is to identify how confident a user is in the system beingable to identify and adjust its operation based on the receivedbehavioral, contextual, and physiological information. For example, aperson may completely trust the system to automatically adapt over timewithout any user intervention while another person may trust the systemto adapt certain aspects of the system (e.g., menu items), but not haveconfidence for adjusting analyte thresholds without approval.

In some implementations, each adaptation identified may be associatedwith a confidence score. The confidence score may be based on one ormore of: the amount of data received from the user that was included inthe determination of the adaptation, the amount of data received fromthe device that was included in the determination of the adaptation, theamount of data received from the community of users or users withsimilar health concerns as the user of the device that was included inthe determination of the adaptation, or a characteristic indicating thelevel of sophistication of usage for the user, for example. Theseamounts may be compared to respective threshold values to determine arelative confidence score for the proposed adaptation. As such, the moredata that is available, the more reliable the adaptation is likely tobe.

Based on the confidence score, and, in some implementations, the userconfidence level (e.g., trust level in the system), the system may beconfigured to automatically apply (within adaptive reporting process200, adaptive goal setting process 500 or adaptive guidance process 600)the adaptation, apply the adaptation after confirmation, or not applythe adaptation at all.

At block 404, the behavior and/or contextual inputs are tracked overtime to collect a database of information (builds record of input). Thesystem may periodically store pre-identified inputs, wherein a record ofuser-specific pre-identified inputs is created. By tracking over time,patterns can be extrapolated. For instance, behavioral informationacquires meaning when a track record of certain use patterns over timeis provided. Similarly, contextual information is traceable andassociated with time (e.g., user typically at school from 9 AM-3 PM).

In some implementations, the tracked data may be collected in aspecially adapted database on the device. In some implementation thestorage may be in data communication with the device (e.g., in the cloudor other devices (e.g., accelerometer, sleep tracker)). The collectionmay be directly from the sensor or via an intermediate module such as acalendar.

The collected information may include information collected by userswith the same condition as the user of the device. For example, for apatient query, the system is configured to respond with a weightedresponse where x% weight is given to the user's past data and y% weightis given to data of other users under the same condition such that thesum of x and y is 100%. Historical referencing and learning based on theuser themselves, and how they compare to a broader population, providesinputs that may be useful to the adaptation process (within adaptivereporting process 200, adaptive goal setting process 500 or adaptiveguidance process 600) to better identify how trend or health projectsforward and what interventions/actions/steps are possible and mostsuccessful at resolving situation or maintaining a desired state.

At block 406, the inputs are processed to determine behavioral and/orcontextual information about the patient. The information may becollected into a record for the patient. The processing may be periodicsuch as according to a schedule, or event driven (e.g., upon receipt ofnew input data, during idle system times). The system is configured toidentify patterns of behavioral or contextual information (e.g., userinteraction with the physiological information display). Theidentification may be based on recognition of predetermined patterns ofinput data (e.g., one or more values or ranges of values associated witha behavior or condition). For example, if the input is an accelerometer,and the detected speed is 70 miles per hour, the speed input informationmay be associated with the behavior of driving. In some implementations,the system may include or communicate with an artificial intelligencemodule adapted to extrapolate contextual and/or behavioral informationassociated with patient. The generated patient information may be usedas contextual and/or behavioral information for further adapting thedevice, such as described above in reference to: block 204 of theadaptive reporting process 200; block 504 of the adaptive goal settingprocess 500; and/or block 602 of the adaptive guidance process 600.

The example method shown in FIG. 4 includes block 408 which provides anindication of the contextual and/or behavioral information identified.Block 408 may be omitted in some implementations. As some informationmay be collected automatically, estimated, or inferred, block 408serves, in part, as a “sanity check” on the information and adaptationsbased thereon. By providing an indication of the identified information,users (e.g., the patient, doctor, caregiver, etc.) can provide somefeedback regarding the identified information. For example, theindication may be provided in the form of a question on a user interfaceor report with a selection of responses or open ended response. Examplequestions include “Were we right?” “Should we act on this information?”or the like.

In some implementations, the indication may be transmitted to anotherdevice, such as an insulin pump, medical record, or other systemincluded in the care plan for the patient. Transmitting the indicationcan help these systems (or the monitoring system) better estimate andunderstand their capabilities, adapting operational characteristicsbased on good estimates/decisions/intuition, inconsistentestimates/decisions/intuition or bad estimates/decisions/intuition. Themonitoring device may be configured to even respond withteachings/learnings to help the other devices improve theirestimates/decisions/intuition.

As one example, an interactive avatar displayed on the device coulddisplay a prompt to the user inquiring, “Are you at home?” or “Would youlike to change your alarm settings when you are at home?” Based on theresponse, the device could inform insulin pump about context or behaviorthat might influence which control algorithm runs, which basal profileto start with, where to send alerts, when to open the loop of a closedloop control system (e.g., adjusting to include validation or transitionto semi- or non-closed state), etc. The device may be configured totransmit this information to the cloud for collection in a database(preferably in de-identified format), or to an electronic medical record

Providing the indication may include speech recognition, taking verbalqueues, and/or use natural language processing. For example, theresponse to the indication may be a delayed “I think so.” Suchindecision may be identified based on the received audio waveform. Thisindecision may be used to discount the indicated information. Forexample, if the behavior identified was “ice fishing” and the receivedresponse was a slow “I think so”, the system may be configured toconsider this behavior, but for limited purposes.

As described, in providing the information, further requests and/orprocessing of additional information/inputs from the user may beperformed based on data processing/feedback loop. This allows theadaptation process to work with the information provided and obtain theinputs most likely to yield meaningful adaptations for the user.

The system can be configured to remind the user that looking athistorical data is an important part of their diabetes management. Thereminder may be included as a message for periodic display via thedevice. In some implementations, the reminder is combined with otherbehavior/context pattern recognition to generate a message for displayto the user about their historical data.

Consider the following example including aspects of FIG. 4. A userinputs data into their personal calendar which is registered tocommunicate the calendar data to the monitoring device. Upon analyzingthe calendar data via, for example, key word searching, the systemidentifies that the user's upcoming events includes exercise, such as aplanned yoga class. The monitoring system may see this class recurringweekly. Accordingly, the monitoring system may adapt to the exercisepatterns and make recommendations such as, “Would you like to start yourtemp basal rate now if you plan on exercising in 1 hour?” one hour priorto the scheduled class. As another recommendation, the system maypresent the user a message such as, “Consider eating a snack now if youare going to exercise in 1 hour.” The message may be selected based onprevious exercise patterns from similar activities. In someimplementations, the input information may be obtained from social mediacalendars or events planning systems, such as Facebook™ events or Evite™invitations.

By determining an upcoming event/behavior/context (within process 400),a corresponding adaptation may be provided. For example, if an activitysuch as sustained exercise, long bike rides, hiking, etc. is identified,a database of recommendations may be queried based on the anticipatedevent as well as other patient specification information to identify anappropriate suggestion and time to provide the suggestion. For example,the system may identify a reminder for the patient to eat a small snackhalf-way through to prevent hypoglycemia and/or refuel. The suggestionmay also include possible snacks based on information from a smartrefrigerator regarding the available food items in the refrigerator.

The monitoring device may be configured to identify locationinformation. The location information may be obtained directly from apositioning device included in or in data communication with themonitoring device. Over time, the location information for a patient maybe used to help understand life patterns and potentially even activity.For example, the locations may be geocoded to identify a business thattakes place at a given location (e.g., gym, Italian restaurant, grocerystore, movie theater, etc.). Location information may be utilized by theadaptive reporting process (200), the adaptive goal setting process(500) and/or the adaptive guidance process (600) to determinelocation-based adaptations and/or goals.

Additional inputs may include audio inputs such as recordings of theuser. The monitoring system may be configured to identify stress, panic,or anger by analyzing the recordings (e.g., wavelength, tempo, pitch,volume, length of conversations). Stress, panic, or anger may also beidentified based on physical behavior. In general, the more animated aperson is, the higher the degree of excitement. Accordingly, motioninformation may be captured by the device (e.g., from an accelerometer)and compared to an activity threshold. The activity threshold may be astandard threshold or adapted to the patient. If the activity leveldetected exceeds the threshold, then the patient may be identified ashigh excitement and thus stressed, panicked, or angry. The thresholdsmay be calibrated for each of these three emotions (or other emotions).

FIG. 5 is a process flow diagram of a method of determining goals orcriteria for use in one or more aspects described. The process adaptivegoal setting process 500 shown in FIG. 5 may be implemented in whole orin part using a continuous monitoring system such as the devices shownand described in FIG. 1. The adaptive goal setting process 500 may beimplemented as a server process in data communication with a continuousmonitoring device. The adaptive goal setting process 500 may beimplemented in hardware such as via a field programmable gate array orapplication specific integrated circuit or a microcontrollerspecifically configured to implement one or more aspect of the adaptivegoal setting process 500 described. The goals or criteria determined bythe adaptive goal setting process 500 can be provided for use in one ormore of the processes described such as block 206 of the adaptivereporting process 200 and/or block 602 of the adaptive guidance process600.

The process of providing automatic adaptation of goals or criteria basedon behavioral and/or contextual information can solve a long felt needof personalizing general purpose devices for healthcare use, or healthcare devices intended for use with a wide variety of user preferences,in a simple and intuitive manner. That is, not all users have the samepreferences when it comes to personalization of goals or criteria ofphysiological information of a medical device, especially aconsumer-driven medical device. Some users may be particularly motivatedand quickly achieve certain goals; while other users may take more time.Many users fall in between and their preferences may be influenced bythe context surrounding their interaction. Unfortunately, creatingdevices that are highly customizable also tend to be highly complex, andvice versa, therefore do not address the full spectrum of users. Thereremains a need for automatically and adaptively understanding thebehavior and context of the user of a consumer-driven medical devicethat is highly intelligent and allows for adaptation of the goals andcriteria. In one implementation, these needs are met by providing apredetermined goal for a user (502); determining behavior or contextualinformation for the user (504); optionally obtaining ongoing behavior orcontextual information for other users (506); adapting goals for theuser and/or optionally for other users (508) and providing an adaptedgoal or criterion (510), a device may be efficiently, intuitively andintelligently personalized for optimizing health care management anduse, without requiring a complex or comprehensive understanding oftechnology, human behavior (or context) and health data that wouldotherwise extremely difficult, inefficient and likely impossible for ahuman to perform as is made possible by the systems and methodsdescribed herein.

“Adaptive goal setting” generally refers to the process, quality, or actof updating or changing the goals or criteria based on receivedinformation such that a goal or criterion is adjusted. In someimplementations, the adaptation is based on predictive inferences drawnfrom the information collected for the associated user. An adaptive goalsetting system or method may be contrasted with a reactive goal settingsystem or method. Whereas a reactive goal setting system or method mayprovide a single reactive adjustment in real time based on a singleevent or selection (e.g., in reaction to a stimulus), an adaptive goalsetting system or method anticipates the event based on the previousbehavioral or contextual patterns identified for the user over time andmakes an ongoing adjustment to a goal or criterion based thereon.

A goal may generally refer to a desired outcome such as more frequentchecks of historical data. For each goal, there may be one or morecriteria indicating whether the goal has been achieved. For example, ifthe goal is increased frequency of historical data checks, the criteriamay include: desired number of checks per unit of time and number ofchecks for previous unit of time. The goal may identify a relationshipbetween the criteria. For example, the goal may include an indicationthat the number of checks for a previous unit of time must be greaterthan or equal to the desired number of checks per unit of time. Morecomplex relationships may be defined for a goal. Goals may be organizedhierarchically such that a higher level goal cannot be achieved withoutfirst satisfying a lower level goal.

Examples of goals include goals focused around setting up the system,goals for setting up thresholds, sensor wear time goals, sensor sessiongoals (e.g., first sensor session completed, first 3 days on sensor),device calibration goals, alarm acknowledgement goals. Some goals may beselectable goals that correlate to other contextual inputs. e.g. don'tgo low when exercising. Goals may be context sensitive. For example, agoal may be based on detected location. For example, don't go low whenat the gym. Further examples of goals include insertion goals such aslearn how to use applicator, how to wash hands, place sensor, startsensor session. Removal/disposal goals such as how to get the most outof the session's data, how to dispose of sensor, may be established andmonitored by the system. Still further examples of goals include datagoals (e.g., how to read data, how to set up alarms), community goals(e.g., how to provide/give support to others), daily goals (e.g., how toaddress eating behaviors, how to address exercise behaviors, sleepbehaviors), device maintenance goals—how to clean transmitter, how tocharge receiver, how to customize transmitter, how to change alarms, howto calibrate), treatment goals (e.g., how to count carbs, identifytarget range, calculate insulin on board).

Another example of a goal includes input specific goals such as forpediatric patients who are moving from having their diabetes managed bytheir parents to achieving greater levels of self-management to preparethem for life on their own. Carb counting goals or use of photo softwarefor counting carbs can be identified for these patients to help patientsactually use the system and take ownership of their diabetes management.As younger users may respond more positively to rewards, the adaptationprocess may also include offering rewards for positive behaviors.Furthermore, the system may be configured to show younger patients howvarious activities affect their BG levels so they can understand thepatterns themselves. For example, a time-lapsed animation may begenerated by the system based on the obtained inputs (e.g., behavior,sensor, and context). To provide peace of mind to the parent, the systemmay include further configuration to transmit the information viaprogress reports for the child. Accordingly, parents can feel morecomfortable with giving their child more autonomy. Autonomy can be atwo-way street because a child patient cannot achieve totalself-management if the parent is consistently managing the child's care.In some circles, such overbearing parents may be referred to as a “hoverparent.”

Similar goals may be established for older patients who are also beingcared for by a family member or care provider. Using the trainingexample, the goals may provide a way for a heath care team to monitortheir patient's progress and provide feedback to other loved ones/familymembers caring for an older adult.

The list that follows identifies further goals which may be provided:

1. How long maintain no hitter?

2. How many consecutive nights without hypo?

3. How many post-meal sessions without breaking a threshold mg/dL?

4. How long keeping rate of change under a threshold mg/dL/min?

5. Less than a threshold number of severe hypoglycemic event during atime period (e.g., in the last month)?

6. Less than a threshold number of hypoglycemic events in a time period(e.g., week)?

7. Less than a threshold number of hyperglycemic events in a time period(e.g., week)?

8. Glucose standard deviation less than a threshold value in a timeperiod (e.g., the last week).

9. Time in target increased by a threshold amount (e.g., percent).

10. Time hypoglycemic event decreased by a threshold amount (e.g.,percent).

11. Time hyperglycemic event decreased by a threshold amount (e.g.,percent).

At block 502, a pre-determined goal is provided. The pre-determined goalmay be installed at the time of manufacture. The pre-determined goal maybe based on the intended use for the device, such as glucose monitoring.The pre-determined goal may be installed when the device is initializedthe patient based on some basic input information received (e.g., age,condition, language). The initial goals may be established based onreceived responses selecting goals from a list of predetermined goals.

In some implementations, a care provider may transmit information via aninterface identifying goals for the patient. For example, a goal may beto work on food behaviors and nothing else, for the next 3 months. Goalsmay be provided via the interface by a member of the patient customersupport (PCS) team working with the patient. For example, the goal mayoriginate from a patient request such as “I can't seem to get the sensorinsertion location to be comfortable.” As one way to assist the patient,the PCS member may transmit a goal to work with how and where the sensoris inserted (e.g., sensor should be moved to right side.).

As described thus far, the goals are associated with a singleuser/patient. In some implementations, patients may be logically groupedinto communities such as online communities, communities of users,social media networks, patient care communities, or the like. Byassociating a goal with the community, users can compete within thecommunity and with other communities to achieve goals. Such socialinteraction can increase usage of the device and enhance overall healthoutcomes.

To identify goals, the device may be configured to operate in a trainingmode. The training mode may be identified as a period of time (e.g., twoweeks from the first power on). During the training mode, the device maybe configured to collect inputs to identify and select a goal from alist of programmed goals. In some implementations, the device may notcollect inputs but utilize a batch record import including behavior andcontext information for the patient. The imported information may bethen used to identify one or more predetermined goals.

In some implementations, the system may include default goals. Forexample, a default goal could be defined which is applicable to a widevariety of potential outcomes, such as glucose variability, time apatient takes to transition from a first glucose level to a secondglucose level (“turn around time”), “no hitter” days, A1c reduction,interaction with the data, consistency of sensor wear, calibrating ontime, insulin delivery requirements, and the like.

As described, information for the device may be received via aninterface. An interface may include, but is not limited to, a visualinterface such as a graphical user interface including one or morefields configured to obtain inputs to the system. In someimplementations, the interface may be a voice interactive interface.

The device may provide a search capability. The device may receive queryparameters for searching the device or other entities in datacommunication with the device (e.g., content library, map server,internet search engine, etc.). As the activity of searching may beincluded as a behavioral input, an adaptation may be based on thequeries submitted. For instance, a new goal may be identified based on anatural language query provided to the system. For example, if a searchis detected including the query “How do I avoid afternoon lows?” acorresponding goal may be set to help the user achieve the goal ofavoiding afternoon lows. The goal may be identified as discussed above,by comparing the received input information to one or more keywordsassociated with the goal.

At block 506, ongoing behavioral and/or contextual information fromother patients/metadata is obtained. As previously discussed,information for the patient and other patients with similar conditionsto the patient may be stored remotely (e.g., in the cloud). Usersidentified as facing an issue may provide suggestions of solutions thathave worked for them. When the system receives a query, (e.g., how do Iprevent a rebound hypo), the system takes their prior cases of reboundalong with all inputs available (e.g., what did the user eat, what wasthe level of different hormones, etc.) and compares it to big data inthe cloud of other users who avoided the same condition and presentactionable recommendations. The comparison may obtain the aggregateddata and perform the matching on the device. In some implementations,the comparison may be performed in a server-client mode. In this mode ofoperation, the query may be augmented with some additional, patientspecific information and transmitted to an analytics engine in datacommunication with the device. Once processed, the response may bereceived by the device. By querying past user and broad population data,a more robust response may be provided.

At block 504, contextual and/or behavioral information for the user iscollected from the user. FIG. 4 provides more detail on how behaviorand/or contextual inputs may be captured, tracked, determined andindicated, any of which may be applied as a subroutine at block 504 ofthe adaptive goal setting process 500. Namely, contextual and orbehavioral inputs may be captured as described at block 402; thebehavior and/or contextual inputs may be tracked over time to collect adatabase of information as described at block 404; and the inputs may beprocessed to determine behavioral and/or contextual information aboutthe patient as described at block 406; based on which an indication ofthe contextual and/or behavioral information may be optionally provideas described at optional block 408; and/or the contextual and/orbehavioral information directly inputted to block 504 described herein.The collection of information may include doctor prescription, PCS teamgoal generated based on a phone call with patients, or a goal the userassesses the need to perfect. Such information may be collected actively(e.g., through responses received through an interface of the devicefrom a user) or passively (e.g., via sensors).

At block 508, based on the information obtained about the patient (block504) and others similarly situated (block 506), one or more goals may beadapted. The adaptation may include altering criteria for a goal. Theadaptation may include identifying additional goals to include for thepatient. The adaptation may include removing a goal for the patient. Theadaptation may include a consideration of who initially defined the goal(e.g., default, doctor, PCS team member, self, or team). For example, ifthe goal was a doctor identified goal, the goal may not be automaticallyadaptable. For such goals, a suggestion of an adaptation may be providedto the doctor and, upon confirmation, applied. Generally speaking, thepatient should select goals and the system should suggest goals. In someimplementations, the system may be configured to receive a patientconfirmation for adding suggested goals and/or adaptations to existinggoals.

The system, in some implementations, may be configured to adapt apredetermined goal based on criteria. For example, if user has achieveda first goal, the goal may be adapted to make the goal a bit tougher. Orif the user does not achieve a goal, the system may adapt the goal toreduce the stringency of a criterion for the goal so that success can beachieved and/or perceived. Rewards for achieving a goal may includebadges. Badges may represent virtual identifiers associated with theuser/patient. Badges may be appointed or a person's profile can beelevated to show new status based on achieving goals.

The system may be configured to automatically identify new predeterminedgoals based on inputs and criteria from other factors, such as food,exercise, insulin intake, etc. as the information becomes available tothe system.

In some implementations, the system may include pattern recognitionmodule. The pattern recognition module may be configured to look fordifferences between the received input for a user and patternsassociated with typical users. For example, the system may consider aprevious data set from a patient (e.g., several weeks of data) andestablish a pattern of glucose behavior as a basis or goal against whichto compare future data. When the glucose reading is identified outsidethe basis or goal determined from the pattern of glucose behavior, analert could be sent to indicate something abnormal was occurring. Thismay be relevant, for example, for type 2 diabetics who have nomedication or are taking an oral medication. The behavior modificationand identification of patterns are substantially different than normalpatients. This pattern deviation may be desirable feature for patientswho have difficulty complying with their care plan.

As an example, consider a person who wears a CGM. A care giver looked atCGM graph on the 4th morning. The glucose reading was higher than normaland more variable. After some discussion, the person realized he hadforgotten his medication that morning. The patient may not consider themissed dose until the care giver asked him about it, even though he hadseen the data. The CGM may be adapted via pattern recognition toidentify the change in pattern and alert the patient.

The adaptive approach techniques described could also be used to setadaptive target glucose levels for patients (e.g., for non-insulin usingdiabetics). The target levels that would display and potentially alertmay be based on the previous history and the goals the patient wastrying to achieve. Rather than setting a static level for an alert, itcould be dynamically changing to reflect improvements and changes to atarget healthy level.

The system may be configured to identify trouble-spots or knowledge gapsbased on the received behavior or context information vis-a-vis goals.Based on the track record, the system may be configured to adapt bycreating goals to help users improve/learn in these areas. The createdgoals could be prioritized (e.g., more important goals to the patient'shealth condition) or random.

At block 510, in some implementations, the adapted goal may bepresented. For example, the adapted goal may be used in the context ofthe adaptive reporting process 200 shown in FIG. 2 such block 206whereby the adapted goal is compared to the behavioral and/or contextualinformation. Presentation of the adapted goal may include a side-by-sidecomparison of the existing goal and the proposed adaptation. A messagemay be received by the system, acknowledging or declining theadaptation. In this way, a user may exercise control over the changesidentified by the system. The acknowledgment may be received via avisual interface such as a web-form or social media network. Thepresentation and/or acknowledgment may occur out of band. For example,an email or text message may be generated including the goals andproposed adaptations. The email may include a unique identifier whichcan be included in a response email message from the user to accept thechanges. In such an implementation, an email server is configured toreceive the message and automatically transmit a message to themonitoring device acknowledging or rejecting the adaptation.

The monitoring device may include a health optimization configurationwhich adjusts the operation of the monitoring device. This configurationvalue may be provided to the adaptation process as an indicator of howmuch the user wants the system to adapt (e.g., optimize) their goalsand/or how much the user wants to interact with the device regardingsetting or confirming their goals.

The presentation of goals at block 510 may, in some implementations,include suggestions by an avatar, selections from a menu. Thepresentation at block 510 may be triggered by an adaptation. Forexample, when an adaptation is identified and/or applied, thepresentation at block 510 may occur. In some implementations thepresentation at block 510 may be based on time of day (e.g., when, basedon received inputs, most issues seen), location (e.g., where, based onreceived inputs, the most issues are seen), weather (e.g., temperaturethat causes the most issues), and the like.

While goals may be presented to the user of the monitoring device, goalsmay additionally or alternatively be presented to an electronic medicalrecord (EMR), to a parent/caretaker, or to a closed loop control system(e.g., for adjusting settings, variables, inputs, alarms, interactionwith patient, etc.). In some implementations, the presentation may alsoinclude providing the goal to members of a group the user is a memberof. In this way, new goals useful to one user may be contributed to thecommunity for use by other users.

FIG. 6 is a process flow diagram of a method of providing patienttraining, improvement in diabetes management, and/or short termrecommendations. Collectively the provided content may be referred to aspatient training. The patient training may be selected based on thegoals/criteria associated with the patient.

The adaptive guidance process 600 shown in FIG. 6 may be implemented inwhole or in part using a continuous monitoring system such as thedevices shown and described in FIG. 1. The adaptive guidance process 600may be implemented as a server process in data communication with acontinuous monitoring device. The adaptive guidance process 600 may beimplemented in hardware such as via a field programmable gate array orapplication specific integrated circuit or a microcontrollerspecifically configured to implement one or more aspect of the adaptiveguidance process 600 described.

The process of providing adaptive guidance based on behavioral and/orcontextual information solves a long felt need of personalizing generalpurpose devices for healthcare use, or health care devices intended foruse with a wide variety of user preferences, in a simple and intuitivemanner. That is, not all users have the same preferences when it comesto personalization of guidance or training of physiological informationof a medical device, especially a consumer-driven medical device. Someusers may be particularly motivated and quickly understand many basicconcepts associated with use of the medical device; while other usersmay require more guidance or training. Many users fall in between andtheir preferences may be influenced by the context surrounding theirinteraction. Unfortunately, creating devices that are highlycustomizable also tend to be highly complex, and vice versa, thereforedo not address the full spectrum of users. There remains a need forautomatically and adaptively understanding the behavior and context ofthe user of a consumer-driven medical device that is highly intelligentand allows for adaptation of the guidance or training. In oneimplementation, these needs are met by identifying a guidance need for apatient based on behavioral and/or contextual information (602);providing guidance to the user (604); and receiving additional queries,behavior, context or physiological information (606), a device may beefficiently, intuitively and intelligently personalized for optimizinghealth care management and use, without requiring a complex orcomprehensive understanding of technology, human behavior (or context)and health data that would otherwise extremely difficult, inefficientand likely impossible for a human to perform as is made possible by thesystems and methods described herein.

“Adaptive guidance” or “adaptive training” generally refers to theprocess, quality, or act of providing guidance based on receivedinformation associated with behavior or context of the user. In someimplementations, the adaptive guidance is based on predictive inferencesdrawn from the information collected for the associated user. Anadaptive guidance system or method may be contrasted with a reactiveguidance system or method. Whereas a reactive guidance system or methodmay provide a single reactive guidance in real time based on a singleevent or selection (e.g., in reaction to a stimulus), an adaptiveguidance system or method anticipates the event based on the previousbehavioral or contextual patterns identified for the user over time andmakes an ongoing adjustment to the guidance or training based thereon.

The adaptive guidance process 600 identifies and provides adaptiveguidance for the patient related to use of the continuous monitoringsystem and/or management of the disease state associated with themonitoring. The guidance may be based on an identified training needbased on received inputs for the patient (behavior and/or contextinformation). The adaptive guidance need may be identified based on aquery submitted by the patient via a search function included in themonitoring device. For example, a natural language query may be receivedby the monitoring device. Textual processing of the query may identifykeywords. The keywords for the query (and, in some implementations,historical keywords) may be used to identify concepts which the patientasks a lot of questions about. The volume of questions may indicate moretraining is needed on the topic. In some implementations, the naturallanguage queries may be received via a provided preselected query formatsuch as “Predict overnight glucose levels and insulin requirements,”“How do I adjust basal insulin settings?”, “How much/type/timing insulinto bolus?”, “How much/type/timing of exercise?”, “Howmuch/when/where/what to eat?

At block 602, a need for guidance is identified based on behavioraland/or contextual information obtained for the user. The obtaining maybe performed as described with reference to FIG. 4 may be provided as asubroutine herein. Namely, contextual and or behavioral inputs may becaptured as described at block 402; the behavior and/or contextualinputs may be tracked over time to collect a database of information asdescribed at block 404; and the inputs may be processed to determinebehavioral and/or contextual information about the patient as describedat block 406; based on which an indication of the contextual and/orbehavioral information may be optionally provide as described atoptional block 408; and/or the contextual and/or behavioral informationdirectly inputted to block 602 described herein. As discussed above, thebehavior and/or contextual information may be received from an externaldevice, collected by a sensor, or determined based on input information.The information may include a query received via the monitoring device.As discussed, in some implementations, weighting associated with eachinformation element may be obtained. For example, contextual informationmay be weighted by a patient's personal preferences such as “I preferorganic foods instead of processed,” “I'm a vegetarian,” or “I likerunning instead of biking.” Based on these preferences, whetherexpressed directly or identified based on behavior and/or context,weights may be adapted and assigned for contextual inputs and how muchinfluence a particular input will have the criteria evaluation processduring the search. Criteria for initiating the training process may bepredefined in the system, wherein certain behavior and/or contextinformation define criteria for identifying a training need of thepatient.

Other questions which may identify the opportunity for additionaltraining include:

1. When/what should I eat or drink to [exercise description here]?

2. What should I do today to meet my goal of losing weight?

3. Recommended restaurants or foods, servings and exercise

4. Can I substitute insulin with exercise? How much?

5. What would be my glucose in [number] minutes?

6. Based on input information received from a navigation system, can Icomplete the route without food?

7. If not, when should I eat? How much?

8. Suggest a restaurant on the route.

9. What is my estimated morning glucose?

10. What do I need to do to make that estimate x?

11. How do I treat this hypo/hyper?

12. What should I buy (or stop buying) at the grocery store?

13. Automatic grocery store list

Additionally or alternatively, a training/guidance need may beidentified at 602 based on the comparison of behavior or contextinformation with a goal or criteria at block 206. For example, if thegoal for a patient is to increase interaction with the device (andoptionally adaptations to the reporting style do not result in progresstoward achieving that goal), the training process may be initiated,whereby a training need (increased interaction with the device) isidentified, and training (adaptive guidance) regarding how to bestinteract with the device is provided (604).

Additionally or alternatively, a training/guidance need may beidentified at 602 based on the adaptive goal setting process 500, whichis based on the determination of behavior or context information (withinbehavioral and/or contextual information determination process 400). Forexample, adaptation of goals (at 508) based on behavior/context mayidentify a training/guidance need (602) and adaptively provide guidance(604) toward the adapted goal. In some cases, wherein the adapted goalis more stringent than the previous goal, a training/guidance need maybe identified (602) that adaptively provides guidance (604) on how toachieve the adapted goal. In some cases, wherein the adapted goal isless stringent than the previous goal (e.g., resulting from continualfailure to meet the previous goal), a training/guidance need may beidentified (602) to provide adaptive guidance (604) to the user inimproving their performance towards achieving the goal. In some cases,wherein the adapted goal is a new type of goal for the patient, thetraining/guidance need maybe identified (602) to provide adaptiveguidance for the patient (604) towards achieving the goal.

As a part of the behavior and/or contextual information gathering, anatural language query may be processed, along with physiologicalinformation and/or behavioral/contextual information. By combining theprocessing of physiological information and behavioral/contextualinformation, processing of queries (e.g., natural language queries orstructured language queries) may be more personally and contextuallyanswered.

Natural language query may take the form of a question received from theuser by the system. For example, “I am hungry, what should I eat?” Anatural language query, however, need not be in the form of a question.Providing a statement may trigger a query based on natural languageprocessing of the received statement. For example, the statement “I amgoing to exercise now” may cause the system to determine that the usertends to hit a glucose low thirty minutes after beginning exercise. Forsuch a user, the system may provide a message suggesting, “You may wantto eat x carbs and I'll set an alert reminder in 3 hours to check yourglucose levels” to help the user avoid the low.

In some implementations, processing the query may include artificialintelligence processing. One artificial intelligence technique which maybe applied is “back propagation.” In back propagation processing,criteria and input for affecting change on the system are weighted. Thesystem dynamically adjusts the weights based on such things as how oftenan input is encountered, inversely decreased if a different weightedinput is increasing, increase/decrease based on the passage of time,proportioned by one or more factors (e.g., user preferences, time ofday, weather).

In the context of natural language queries, the system is configured, insome implementations, to adapt the weighted criteria. The weighting maybe based on the number of time a question was repeated, the number oftimes a food was purchased based on system's recommendations, and/orturn off a recommendation for meats after some number of requests forvegetarian diet/restaurants.

At block 604, an adaptive guidance (and/or adaptive training) isprovided for the identified need based on the behavior and/or contextinformation for the patient. In some implementations, e.g., based on theidentified need, physiological information of the patient in conjunctionwith the behavioral and/or contextual information is considered inprocessing the adaptive guidance. By considering both physiologicalinformation as well as behavioral/contextual information from thecontinuous monitoring system, training and/or guidance can be morepersonalized without bothering the user with a plurality of questions tobe answered. In implementations featuring a structured language query,only the initial question is structured and the remaining informationmay be provided as part of the query is determined or collected asdescribed above.

The adaptive guidance generated at block 604 may include answers tonatural language query, e.g., recommend a salad from one of thefollowing local restaurants: x, y or z; drink X ounces of Y in 1 hour;etc. The recommendation may be based on a query of a database ofinformation coupled with the monitoring device. In some implementations,the query may be transmitted to a server for processing and the responsereceived by the monitoring device.

The guidance may be provided not only to the user initiating the querybut also to a parent, a child (for older adults), a loved one, a doctor,a certified diabetes education (CDE), a health insurance company,support group in social media, etc. For any age user (adult orpediatric) providing family members, loved ones, or heath care team thisinformation may help provide those uses with a greater peace of mind,lessen the “diabetes police,” and help a child obtain more autonomy fromtheir parents, if the parent is comfortable with the child'straining/diabetes knowledge progression.

The guidance may include activating features of the monitoring devicesuch as placing a call to a doctor or a spouse/parent/friend. A featureof the system may be activated in response to a prompt. The system mayselect the prompt based on the behavioral and/or contextual informationfor the user. Example prompts include:

“Are you going to exercise now? You may want to eat x carbs and I′ll setan alert reminder in 3 hours to check your glucose levels”

“Did you just eat?” About how many carbs were in the meal? Was it asimple carb meal or a complex meal with fat/protein? Consider takinginsulin or x units of insulin.”

“Are you driving? We suggest taking a brief break to have a snack.”

In some implementations the training guidance and/or response may beprovided through an avatar, which guides the user through training,tips, assistance and/or otherwise provides information useful to helpanswer the patients' question and/or provide training to address theidentified training need.

At block 606, additional natural language, behavioral and/or contextualfeedback may optionally be received. The feedback may include commentson the response. The comments may be transmitted via the monitoringdevice. The system may prompt the user for comments to get as muchpersonalized information as possible from the user without requiringexplicit user interaction (i.e., without necessarily asking directquestions). As shown in FIG. 6, the adaptive guidance process 600 mayreturn to block 604 for further processing.

The feedback information may help refine a query. For example, expectedduration of exercise, level of exertion, what did you have forbreakfast, carbohydrate in-take, or a relative state (e.g., sick,stressed, excited on a scale from 1-10) may be obtained. The system maybe configured to obtain the additional information and re-process thesearch with the additional information. In some implementations, theprompt for additional information may be based on the number of resultsfor the initial query. For example, if too many hits are found for thequery “how do I reduce my weight” for the patient, feedback on preferredexercises or foods may be obtained to help narrow the results.

The described methods and systems may be applicable for diabetesmanagement. In the context of diabetes management, personalizedassistance in maintaining analyte levels within a target range orgetting to a desired target range is provided by the described features.Pre-specified queries related to: how to adjust basal insulin settings,how much/type/timing insulin to bolus, how much/type/timing of exercise,and/or how much/when/where/what to eat may be included in the system.The system may process input information from a variety of sources suchas caloric intake derived from photo analysis software, a body metricthat can be measured or unique shapes to glucose trend graphs, exerciseinformation from a fit bit or the like, glucose sensor information fromBG meter or CGM, insulin delivery amounts from insulin delivery device,insulin on board calculation and/or other device provided or calculatedinformation. The systems and methods may also obtain information frompersonal calendar or social media sites the person is associated with.The adaptations are based on machine learning of patterns such asweights. The system and methods further include feedback which allowsrequests for additional information (e.g., Where are you located?).Aspects described further provide outputs including answers to question(e.g., recommend a salad from one of the following local restaurants: x,y or z.).

It has been shown that people who look at historical glucose data havebetter diabetes management. One non-limiting advantage of the describedsystem and methods includes capturing a time stamp of how often peoplelook at their historical glucose data. If a user has not looked at theirhistorical data in a given period of time, the system can adapt thetype, frequency, and format of reminders for the user. The reminder caninclude information that looking at historical data is an important partof their diabetes management.

This could be combined with other pattern recognition to generateinformation about the user's historical data.

Diabetes management may be performed by a person through their behaviorand actions. The adaptations and feedback may be used to help peoplemanage their condition. Diabetes management may also be implementedusing control systems such as closed loop control systems. By providingbehavioral and contextual information for one or more users of thesystem, the system can adapt overtime to provide more effective,accurate, and tailored management of an individual's condition.

The described features may be applied in the context of non-diabeticsand type 2 weight loss. A simple target calorie intake may be determinedbased on your calorie expenditure. Goals may include losing a certainnumber of pounds over a period of time. If your goal is to lose 10pounds in 3 weeks, you would need to eat 500 calories less than you burnper day. Each day, the calorie expenditure is recorded and the nextday's calorie intake is adaptively generated.

This can be extrapolated to glucose targets for type 2 non-insulin usingdiabetics. The system or method may be configured to analyze theglucose, calorie, and exercise behavior and/or context information.Based on a comparison of the received information and the goals and atarget may be generated for the next day. For example, the system maygenerate and provide the following recommendation to achieve a glucosetarget, today try and work out for an additional 10 min and reduce yoursugar intake by 5%. The recommendation may be calculated based on thepatient specific input information, including weighting of the specificfactors considered.

Athletic performance optimization implementations may incorporate one ormore of the features described above. For example, a monitoring devicemay receive a natural language query such as, “When/what should I eat ordrink to run 6 more miles (or even starting this question before theperson begins to exercise, what do I need to eat/do with my insulin nowto exercise in 1 hour)?” In the performance optimization context,behavioral and/or contextual inputs may include one or more of:hydration level, heart rate, target heart rate, internal temperature,outside temperature, outside humidity, analytes in the body, hydrationinputs, or power output (cycling). The system may be configured toprocess the query using natural language processing, data mining, and/ormachine learning to process the query in conjunction with the providedinputs/behavioral information. In some implementations, the query may berefined based on additional feedback, such as: expected duration ofexercise, level of exertion, what did you have for breakfast, etc., carbintake. Based on the received input information, the system may beconfigured to generate a recommended athletic performance plan such as:drink X ounces of Y in 1 hour.

A further example of an area which may include the features described isa weight loss monitor. In this example, the monitor may receive a query(natural or structured) such as “Should I do today to meet my goal oflosing weight?” or “Can you recommend restaurants or foods, servings andexercise?” The monitor may also receive physiological inputs such asglucose level, lactate, free fatty acids, heart rate during exerciseand/or IgG-anti gliadin In conjunction with the physiological input, thesystem processes behavioral and/or contextual information (e.g.,including current weight, BMI, desired weight, target calories per day(consume), target calories per day (expend), location, favorite foods,level of exertion). The processing provides a recommended schedule ofactivities and food based on received inputs.

FIG. 7 is a functional block diagram for a continuous monitoring deviceincluding an adaptive interface. The device 700 includes an inputreceiver 702. The input receiver 702 is configured to obtain behavior,context, or physiological input values. The values may be received fromsensors, information systems, social media, voice response, actions onthe device 700 (e.g., search), and other described above. The inputreceiver 702 may include wired (e.g., Ethernet, USB, HDMI, coaxialcable, telephonic, patch cabling, fiber optic cable) or wireless (e.g.,WiFi, Bluetooth) communication means for obtaining the inputinformation.

As shown in FIG. 7, the input receiver 702 is coupled with an inputprocessor 704. The input processor 704 may be configured to process thereceived inputs. The input processor 704 may be configured to processthe received inputs based on processing rules obtained from a processingrules database 706. The input processor 704 may receive the input dataalong with the source of the data. Based on the source, a processingrule may be selected from the processing rule database 706. Theprocessing rule may indicate a format for the input data, an appropriateparser, or other information to facilitate extraction and categorizationof the information included in the input data.

The input processor 704 may provide the extracted information to aweighting processor 708. The weighting processor 708 is configured todetermine a weight for each value extracted from the input data. Asdiscussed above, some inputs may influence the adaptation process moreheavily than others. The weighting processor 708 is configured toidentify these weights. The weighting processor 708 may be in datacommunication with a historical input value database 710. The historicalinput value database 710 includes past input values for the user. Insome implementations the weighting processor 708 may transmit a requestfor a weighting to a remote server configured to generate the weightingbased on aggregated input information for a plurality of users (e.g.,big data analytics for a community of similarly situated users).

The weighted input values may be provided to an adaptation engine 712.The adaptation engine 712 is configured to identify and apply one ormore of the adaptations discussed above such as device adaptations, goaladaptations, interface adaptations, training or content adaptations. Theadaptation engine 712 may identify one or more adaptations included inan adaptation rules database 714. The adaptation rules database 714 mayinclude one or more adaptations to apply based on the input value. Forexample, if an input value indicates low glucose, a set of adaptationsmay be stored in the adaptation rules database 714 indicating possibleadaptations to increase glucose levels.

The adaptation engine 712 shown in FIG. 7 is in data communication witha content database 714. The content database 714 may include categorizedcontent which is searchable based on the input values. Accordingly, amultimedia video may be stored along with a glucose threshold level(e.g., low). Based on an input glucose reading, the video may beretrieved from the content database 714 for presentation.

The adaptation engine 712 shown is also in data communication with agoal database 716. The goal database 716 may be configured to storegoals and associated criteria. The goal database 716 may be configuredto store template goals with pre-determined criteria. The goal database716 may be configured to store goals for specific users or user groups.The adaptation engine 712, may identify an adaptation rule whichindicates setting or adjusting an existing goal.

As shown in FIG. 7, the device 700 includes a transmitter 718. Thetransmitter 718 may receive the adaptations identified and transmit thechanges. The transmitter 718 may include wired (e.g., Ethernet, USB,HDMI, coaxial cable, telephonic, patch cabling, fiber optic cable) orwireless (e.g., WiFi, Bluetooth) communication means for transmittingadapted information.

In some implementations, the transmitter 718 may be configured tocommunicate the proposed adaptation before applying the adaptation. Insome implementations, the transmitter 718 may be configured to transmitthe adapted message (e.g., alert, text message, email, FTP, HTTP, orother).

As shown in FIG. 7, the device 700 also includes a goal tracker 720. Thegoal tracker 720 is configured to determine the status of a goal basedon the received input. For example, upon receipt of glucose data, thegoal tracker 720 may retrieve all active goals for the user associatedwith the glucose data. The goal tracker 720 may then determine whetherthe input value received satisfies the glucose level criteria includedin the identified goals. The result of the determination may be providedto the transmitter 718 for transmission. In some implementations, thetransmission may include transmission of a reward as described above.

The connections between the elements shown in FIG. 7 illustrateexemplary communication paths for the device 700. Additionalcommunication paths, either direct or via an intermediary, may beincluded to further facilitate the exchange of information for thedevice 700. The communication paths may be bi-directional communicationpaths allowing the elements shown to exchange information.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

As used herein, the term “message” encompasses a wide variety of formatsfor transmitting information. A message may include a machine readableaggregation of information such as an XML document, fixed field message,comma separated message, or the like. A message may, in someimplementations, include a signal utilized to transmit one or morerepresentations of the information. While recited in the singular, itwill be understood that a message may becomposed/transmitted/stored/received/etc. in multiple parts.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure (such as the blocks of FIGS. 2and 7) may be implemented or performed with a general purpose processor,a digital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array signal (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects computer readable medium may comprisenon-transitory computer readable medium (e.g., tangible media). Inaddition, in some aspects computer readable medium may comprisetransitory computer readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’including but not limited to,' or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ containing,' or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ desired,' or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for adaptive configuration of an analytemonitoring device, the method comprising: transmitting a first report ofphysiological information of a host using a first reporting format,wherein the first reporting format comprises a first reporting formatcharacteristic; determining at least one of behavioral or contextualinformation comprising at least one behavioral and/or contextualcharacteristic of the host; comparing the at least one behavioral and/orcontextual characteristic with one or more behavioral or contextualcriteria; adjusting the reporting format based at least in part on saidcomparing, wherein the reporting format comprises a second reportingformat characteristic that is different from the first reporting formatcharacteristic; and transmitting a second report of physiologicalinformation using the second reporting format.
 2. The method of claim 1,wherein the first report comprises a trend graph of the physiologicalinformation over a period of time.
 3. The method of claim 1, furthercomprising transmitting the determined behavioral or contextualinformation about the host.
 4. The method of claim 1, whereindetermining at least one of behavioral or contextual informationcomprises: receiving a message from a sensor including data associatedwith the host; identifying a characteristic extractor based on themessage and the sensor; generating, via the identified characteristicextractor, the at least one behavioral or contextual characteristicbased on the received message; and associating the generatedcharacteristic with the behavioral or contextual information.
 5. Themethod of claim 1, wherein determining at least one of behavioral orcontextual information comprises: capturing values from pre-identifiedinputs, the values indicating a behavior or context associated with aphysiological condition for the host; periodically storing additionalvalues received from the pre-identified inputs, wherein a record ofhost-specific pre-identified input values is created; and periodicallydetermining behavioral or contextual information about the host based onthe record of host-specific pre-identified input values captured overtime.
 6. The method of claim 4, wherein the pre-identified inputsinclude at least one of a glucometer, a thermometer, an accelerometer, acamera, a microphone, a query processing engine, an electronic deviceconfigured for machine-to-machine communication, or an electronicpatient record.
 7. The method of claim 4, wherein periodically storingadditional values comprises storing a timestamp indicating when aspecific additional value was stored.
 8. The method of claim 4, whereinthe physiological condition comprises one or more of diabetes, obesity,malnutrition, hyperactivity, depression, or fertility.
 9. The method ofclaim 4, wherein determining at least one of behavioral or contextualinformation comprises: selecting one of a plurality of pre-identifiedinput values included in the record; and identifying one or morebehaviors or contexts based on a comparison of the selected input valueand the input providing the selected value with an identification valueassociated with a plurality of behaviors or contexts.
 10. The method ofclaim 4, wherein determining at least one of behavioral or contextualinformation comprises processing the pre-defined input values includedin the record.
 11. The method of claim 4, wherein processing the valuescomprises identifying a trend for the values.
 12. The method of claim 1,wherein comparing the at least one behavioral and/or contextualcharacteristic with one or more behavioral or contextual criteriacomprises comparing the at least one behavioral and/or contextualcharacteristic with a behavioral or contextual criteria associated witha goal.
 13. The method of claim 12, wherein the goal is selected fromthe group consisting of interaction with the device, amount of time intarget, amount of time outside of target, device location, dataretention, calibrating frequency, standard deviation, patternmanagement, time spent on certain screens, time spent hypo, time spenthyper, time spent at high rates of change, or time spent at low rates ofchange.
 14. An electronic device for monitoring a glucose concentrationin a host, the device comprising: a continuous glucose sensor, whereinthe continuous glucose sensor is configured to substantiallycontinuously measure the glucose concentration in the host, and toprovide continuous sensor data associated with the glucose concentrationin the host; and a processor module configured to: transmit a firstreport of physiological information of a host using a first reportingformat, wherein the first reporting format comprises a first reportingformat characteristic; determine at least one of behavioral orcontextual information comprising at least one behavioral or contextualcharacteristic of the host; compare the at least one behavioral and/orcontextual characteristic with one or more behavioral or contextualcriteria; adjust the reporting format based at least in part on saidcomparing, wherein the reporting format comprises a second reportingformat characteristic that is different from the first reporting formatcharacteristic; and transmit a second report of physiologicalinformation using the second reporting format.
 15. The device of claim14, wherein the first report comprises a trend graph of thephysiological information over a period of time.
 16. The device of claim14, wherein the processor module is configured to determine at least oneof behavioral or contextual information by: receiving a message from asensor including data associated with the host; identifying acharacteristic extractor based on the message and the sensor;generating, via the identified characteristic extractor, the at leastone behavioral or contextual characteristic based on the receivedmessage; and associating the generated characteristic with thebehavioral or contextual information.
 17. A system for adaptiveconfiguration of an analyte monitoring device, the system comprising: aninput receiver configured to receive at least one of contextinformation, behavior information, or physiological information for ahost over a period of time; an input processor configured to identify acontext or behavior based at least in part on the information receivedover time; and an adaptation engine configured to provide adaptivereporting for an analyte monitoring device based on the identifiedcontext or behavior by: transmitting a first report of physiologicalinformation of the host using a first reporting format, wherein thefirst reporting format comprises a first reporting formatcharacteristic; determining at least one of behavioral or contextualinformation comprising at least one behavioral and/or contextualcharacteristic of the host; comparing the at least one behavioral and/orcontextual characteristic with one or more behavioral or contextualcriteria; adjusting the reporting format based at least in part on saidcomparing, wherein the reporting format comprises a second reportingformat characteristic that is different from the first reporting formatcharacteristic; and transmitting a second report of physiologicalinformation using the second reporting format.
 18. The system of claim17, wherein the first report comprises a trend graph of thephysiological information over a period of time.
 19. The system of claim17, wherein the input processor is configured to identify a context orbehavior based at least in part on the information received over timeby: receiving a message from a sensor including data associated with thehost; identifying a characteristic extractor based on the message andthe sensor; generating, via the identified characteristic extractor, theat least one behavioral or contextual characteristic based on thereceived message; and associating the generated characteristic with thebehavioral or contextual information.